This PDF file contains the front matter associated with SPIE Proceedings Volume 12552, including the Title Page, Copyright information, Table of Contents, and Conference Committee Page.

A sudden water pollution accident poses a serious threat to the safety of water supply from reservoirs. In this paper, we built a model of sudden water pollution accident in the Songbaishan Reservoir, designed a pollutant leakage scenario and a discharge flow scheduling scenario, simulated the rules of influence from pollutant leakage on the water quality in the reservoir, and analyzed the effects of different regulated discharge flows on the water quality in the reservoir. The research results indicated that: (1) The EFDC-based model of sudden water pollution accident in the Songbaishan Reservoir can accurately simulate the evolution process of hydrodynamics and water quality in the Songbaishan Reservoir; (2) What follows the sudden water pollution accident is the pollutant mass, and as the movement distance of the pollutant mass increases, the maximum concentration of the pollutant mass decreases and the exceeding-standard time of each cross section increases; (3) Regulating the discharge flow can reduce the impact of pollutant leakage on the reservoir water quality, and increasing the discharge flow can reduce the exceeding-standard time of the water quality in the cross section of the drinking water supply intake. The research results may provide technical support for emergency treatment of sudden water pollution accidents in the Songbaishan Reservoir.

Transmission line path selection is an important part of transmission line engineering design, which directly affects the construction cost and disaster resistance of transmission line engineering. At present, designers mainly rely on the human eye to identify and judge sensitive objects in 3D satellite cloud images, which is not only time-consuming and labor-intensive, but also easily leads to missed judgments and misjudgments. This paper uses the YOLOv5 model and learns from a large number of samples to quickly and accurately judge, identify and label various sensitive objects in the 3D satellite cloud image, which can effectively improve the design efficiency of transmission lines and reduce design costs.

Long-term exposure to higher concentrations of PM_2.5 pollution may cause harm to the physical and mental health of humans. Clarifying the spatiotemporal characteristics of PM_2.5 concentration and its population exposure risk (PER) can provide scientific reference for improving the regional habitat environment and reducing air pollution damage to human health. Here, this paper adopted the Sen's nonparametric method, Mann-Kendall test, Hurst exponent, and population exposure risk model to analyse the spatial and temporal variations of PM_2.5 pollution and its exposure risk in Shandong Province (SDP) based on PM_2.5 concentration and population data during 2000-2020. The results showed that: (1) Spatial distribution of PM_2.5 concentration in SDP showed “high in the west and low in the east”, with an interannual fluctuation trend of “significant increase - fluctuating decrease - significant decrease” during 2000-2020. The area with a downward trend of PM_2.5 concentration accounted for 59.419% of the whole province, and the area where air quality will be further improved in the future accounted for about two-thirds. (2) The annual mean value of PER to PM_2.5 pollution in SDP was fragmented in spatial distribution, with low risk dominating in most areas, while the risk areas were mainly distributed in densely populated urban areas. The area with a downward, unchanged, and upward trend of PER to PM_2.5 pollution accounted for 61.827%, 0.423%, and 37.750% of the whole province, respectively. In addition, PER to PM_2.5 pollution will be further reduced in 55.633% of SDP in the future.

With the rapid development of urbanization, the increase of population, also in the rapidly expanding city construction, so the town has changed in the surrounding land use types, a large number of farmland, forest land into construction land, cities affected by the surrounding environment, so the reasonable planning of land use, so it is urgent to prevent the ecological environment destruction caused by urban construction. Remote sensing technology and geographic information system technology use satellite remote sensing means to monitor cities. In recent years, many scholars at home and abroad have used remote sensing technology to analyze land use, provide data support for urban sustainable development, and help cities do a good job in ecological planning. Through remote sensing technology, this paper analyzes the land use situation of the main urban area of Chongqing in recent 20 years, and provides the basis for the construction planning of mountain city.

With the popularity of mobile devices and the advent of the Internet era, social media data has received widespread attention as a new disaster data source. Based on Twitter data, this paper constructs a research framework for storm and flood public response and disaster detection and monitors the spatio-temporal changes in public sentiment and driving factors. The following conclusions were obtained: (1) There is a strong correlation between the activity of public response to heavy rainfall and flooding on social media and the amount of rainfall, which proves the interconnectedness between the two. (2) Public sentiment changes in a "U" shape and reaches its lowest value during the most severe rainfall and flooding. (3) The public's response to heavy rainfall and flooding is concentrated around the more severely affected areas such as Sydney and the Gold Coast.

In the paper, it makes use of 20546 daily consumption places in the main urban area of Kunming in 2021 as the basic data. Moreover, such places are divided into four categories: shopping service consumption places, living service consumption places, sports and leisure service consumption places and living service consumption places. On the basis of GIS analysis software (ArcGIS), the methods of average nearest neighbor analysis, nuclear density analysis, spatial autocorrelation analysis and spatial pattern abstraction are used to study the spatial clustering characteristics, spatial differentiation and distribution patterns of daily consumption in the main urban area of Kunming. The results show that 1) daily consumption places in the main urban area of Kunming are highly clustered in the core area and generally unevenly distributed; 2) Daily consumption places in the main urban area of Kunming show a decreasing-rising-decreasing “slanting spoon” curve spatial pattern from the center to the periphery; 3) Daily consumption places and the four categories of consumption places have the same spatial distribution pattern generally and the spatial heterogeneity of the density of consumption places in the core area and the second ring area is the most significant.

In order to improve the ability of earthquake prevention and disaster reduction in Shandong Province and reduce earthquake losses, this paper conducted earthquake disaster risk assessment for Shandong Province based on natural disaster risk theory and GIS technology. Based on natural disaster risk theory and methods, 14 indicators were selected from the hazard, exposure, vulnerability, and disaster prevention and mitigation capability to establish the earthquake disaster risk assessment index system. The risk assessment model of earthquake disaster was established by using the analytic hierarchy process - entropy weight method. With GIS technology, the hazard, exposure, vulnerability, and disaster prevention and mitigation capability and integrated risk index in Shandong were calculated and earthquake disaster risk in Shandong was zoned. The results showed that the areas around the Tan-Lu and Liao-Kao fault zones were the medium and high risk areas, mainly affected by the hazard; some economically developed areas were the higher risk areas, such as Shinan district and Shibei district, mainly affected by the exposure of the disaster-bearing body.

As the main component of urban problems, urban waterlogging disasters restrict the development of urbanization and the sustainable development of cities. Based on the three core characteristics of resilient cities, including adaptability, coping ability and recovery, this paper uses an entropy method to establish an urban resilience evaluation index system for waterlogging disasters, which consists of disaster-causing, disaster-resistant and disaster-bearing factors. Then, the resilience index of distinct districts in Shenzhen against waterlogging disasters is analyzed based on GIS technology, and the relevant improvement strategies are proposed. The results show that the resilience level from high to low is Baoan District, Futian District, Nanshan District, Longgang District, Longhua District, Luohu District, Guangming District, Pingshan District, Dapeng New District and Yantian District, and the urban resilience level can be improved by updating urban drainage facilities, strengthening ecological protection and environmental construction, and improving urban disaster-bearing soft systems.

Through a technical study on the selection of scenic trail routes for scenic areas in shallow mountain areas, this paper explores the feasibility of realizing thematic planning of scenic areas and enhancing scientific-ness in the context of territorial space planning with the help of information technology, and provides the basis for the extensive application of geographic information technology in the field of scenic planning.

Traditional researches have reduction strategies of poverty. With the application of concepts such as “ability” and “risk” in the field of poverty research, households’ vulnerability has become new perspective of poverty research because of its forward-looking perspective in recent years, it can also effectively reduce local residents' dependence on local natural resources, reduce environmental pressure and improve environmental quality. Livelihood vulnerability research can provide risk early warning and achieve targeted assistance prediction for the livelihood vulnerability of households. It also influences the sustainable development of economy and ecology. Studies of vulnerability to poverty are mainly aimed at the meaning and the measurement of poverty and ecological protection. Domestic literatures combined with actual situation have mostly drawn lessons on foreign to empirical tests with relevant survey data. Taking northern Tibet in Tibet Autonomous Region as the research area, this paper constructed an evaluation index of vulnerability of poor households from three aspects: risk, livelihood capital and adaptability. We used household index data to analyze the key factors that affect the vulnerability of poor farmers based on the comprehensive index method and according to the divisions of sustainable livelihood framework of livelihood capital. The aim of the study was to provide references for effectively distinguishing vulnerable groups of poor farmers, and establishing and adjusting the policy of rural poverty alleviation, and, at the same time, it can effectively improve the local ecological environment and enhance the environmental ecological carrying capacity.

In this study, a complete set of solid waste management model is proposed for the environmental problems caused by the rapid increase of solid waste production and the imperfect management mode of small and medium-sized cities in the process of urbanization. The model consists of database construction and management scheme planning. The database includes spatial data and attribute data, and the management scheme is the process of resource utilization and terminal processing and disposal in the late management. This set of solid waste management model not only focuses on the planned region, Ankang City, Shaanxi Province, but also focuses on the process and difficulties of database construction and scheme planning, which is especially reflected in the combination of field research and remote sensing analysis in database construction to obtain local road data, community division, solid waste information, etc. The solid waste is divided according to the nature and use of various types of solid waste from the source, and GIS is applied to the scheme design to form a set of solid waste treatment mode supported by GIS that is more suitable for Ankang and similar small and medium-sized cities in the process of urbanization, so as to provide theoretical and data support for solid waste management in Ankang, to realize the optimal control and resource utilization of each step of solid waste from generation to terminal treatment, and serve as a reference for solving some solid waste problems in the development of other small and medium-sized cities in the process of urbanization.

By using daily temperatures of 36 meteorological stations from 1980 to 2020 in Sichuan Province, remote sensing data of DMSP (Defense Meteorological Satellite Program) night light and land use, as well as spatial distribution gridded data of GDP (Gross Domestic Product) and population, we calculated 16 ETIs (Extreme Temperature Indices). With the aid of comprehensive methods of K-means and hierarchical clustering, meteorological stations were divided into urban, suburban, and rural stations. The change trends of extreme temperature events and the effects of urbanization in Sichuan Province were analyzed. The results demonstrated that extreme high temperature events showed an overall increasing trend, while extreme low temperature events showed a decreasing trend. DTR (Diurnal temperature range) and GSL (Crop growing season) indices resulted in an overall upward trend. Due to the complex topography and climate diversity, and with the continuous acceleration of urbanization in the past 40 years, extreme high temperature events in urban and suburban stations were significantly higher as compared to rural stations. However, in these stations, the decreasing trend of extreme low temperature events was more significant as compared to the others. In urban and suburban stations, urbanization significantly impacted the change trends of TXx (Max Tmax), TXn (Min Tmax), TNn (Min Tmin), ID0 (Ice days), TR20 (Tropical nights), DTR, and GSL. In addition, the contribution rates of urbanization were all more than 50%, with little impact on the change trends of TNx (Max Tmin), FD0 (Frost days), TN10P (Cool nights), TX10P (Cool days), TN90P (Warm nights), and TX90P (Warm days). Moreover, no effect on SU25 (Summer days), WSDI (Warm spell duration indicator) and CSDI (Cold spell duration indicator) change trends were found.

Based on GIS method and data from every five years from 2000 to 2020, this paper shows the distribution of ecological vulnerability in Nagqu using the ecological vulnerability index (EVI) and discusses its potential driving factors. The areas with high EVI aggregated in the southeastern Nagqu surrounding Seni district and the western Nagqu such as Nima County. In the rest of the areas except for Shuanghu County, there had been a sharp increase in EVI. To find the driving factors for the distribution pattern and trend, we choose the most weighted factors: population, animal husbandry, and economy. With analyses of these factors, we conclude that animal husbandry might account for the long-term distribution of ecological vulnerability in Nagqu. The changes in population and economy have a similar trend to the change in EVI, thus we conclude the two factors are possibly responsible for the recent EVI change. We also find that the relative vulnerable areas have different driving factors of vulnerability. In southeastern Nagqu, population and economy are the main factors. While in the west, animal husbandry plays more role. This indicates that different methods of management should be considered in different areas, and lower-scale studies are needed for future management.

Taking the Shandong area as the study area, we use the SBAS-InSAR to process 647 views Sentinel-1A data in Shandong area between 2018.7 and 2020.4 and, according to the deformation amount and trend, reveal the hidden geological disaster points and extract the surface deformation field. In this paper, the areas with obvious ground deformation in Feicheng were selected. We combined with the field survey results, finally verified the combination of high-resolution imagery and DEM has a certain effect in the interpretation of geological disaster points.

In this study, based on the Landsat satellite images of Google Earth Engine (GEE), the temporal and spatial characteristics of NDVI, FVC and LAI in Zhejiang Province from 1985 to 2021 were studied. The results showed that: (1) The vegetation condition in Zhejiang Province has been significantly improved. The vegetation coverage increased significantly, the growth status continued to rise, and the growth density continued to expand. (2) The overall vegetation status of Zhejiang Province in the past three decades showed a downward trend first and then an upward trend. (3) There are obvious regional differences in vegetation distribution, that high levels in western and southern mountains, low levels in northern plains and eastern coastal areas. (4) Vegetation in mountainous areas has increased significantly and expansion of low vegetation index areas around cities has slowed.

This work utilizes Sentinel-2A L1C remote sensing photographs from the years 2018, 2020, and 2022 to identify the different land use categories in the study area using the support vector machine (SVM) technique. The accuracy of categorization is greater than 90%. This research explores four factors of the dynamic change in land use in Hongta District from 2018 to 2022: the proportion of various types of land; the extent of something like the changing land usage; land use transfer; and the dynamic degree of the change in land use. According to the study's results, the proportion of cultivated and grassland land grew, while the quantity of barren and construction land fell by 1.90 percent, 0.03 percent, and 0.69 percent, respectively. The water system land portion of total area increased by 2.58 percent and 0.13 percent, respectively. After comparing the two research periods, the entire dynamic degree of the second stage is determined to be 3.5 percent lower than that of the first stage, and the pace of land use change is quite sluggish, which may be associated with the worldwide COVID-19 outbreak in 2020. The outcomes of the research may give the natural resources department the knowledge it needs to manage land resources properly.

Ecosystem services are closely linked to human well-being and therefore are receiving increasing attention. Industrial water shortage and resource-based water shortage coexist in the watershed area of central Guizhou Province. Taking Guanshanhu District in the central Guizhou Province as the research object, this article applied remote sensing images, meteorological data and soil datasets as data sources, with the help of several empirical models and GIS spatial analysis to analyze the change of ecosystems and their water yield services in 2000, 2010 and 2020. The results showed that the artificial surface had been growing rapidly, from 1146.82 hm² in 2000 to 7544.29 hm² in 2020. The area of farmland ecosystem had decreased continuously from 13308.29 hm² in 2000 to 6342.33 hm² in 2020. The area of forest and grassland ecosystem had increased from 14876.12 hm² in 2000 to 17205.08 hm² in 2010, but decreased to 13937.54 hm² in 2020. The wetland ecosystem had no significant change. The water yield depth ranks as: artificial surface < farmland < grassland < forest < wetland. The total water yield in 2020 was the largest, which was 2.13 × 10⁸m³. The ecosystem contributed the most to the total water yield in 2000 was the farmland, which was 0.90 × 10⁸m³. In 2010 and 2020, the ecosystem contributed the most was the forest ecosystem, which was 0.86 × 10⁸m³ and 0.92 × 10⁸m³ respectively. Quantitative research on water yield services can provide strong support for regional water ecological security construction and regional water resources management.

Highway is a part of transportation, which has the characteristics of wide radiation range and long distance. As an artificial landscape, it has a great impact on the ecological environment along the line. According to the impact of Expressway on the ecological environment along the line, this paper constructs the evaluation index system of ecological security of Expressway in mountainous areas. By using 3S technology, using remote sensing data, terrain data and other data, combined with analytic hierarchy process and spatial multi-criteria model, the risk assessment of road disasters is carried out. This study explores the space-time characteristics and risk assessment of highway ecological security, which can objectively and truly carry out pre-disaster early warning, post-disaster auxiliary rescue and disaster damage assessment, and provide effective support for the formulation and implementation of road disaster emergency response and rescue plan.

Nowadays, spatial geographic data analysis and GIS related software are more and more applied to the planning of urban public facilities. Under the COVID-19, people pay more attention to the protection of medical facilities for people's health, and a reasonable distribution of hospital facilities is conducive to people's health. Taking Haikou City as an example, this research will optimize the location of hospital space layout according to the existing third-level first-class general hospitals in Haikou City by using GIS software, road analysis, spatial analysis, and other methods. The results show that the existing hospitals in Haikou are too concentrated in the central urban area, the overall distribution of medical facilities is lack of balance, and there is a serious lack of medical facilities in new urban development areas and suburbs. According to the comparison between population density analysis and traffic analysis and the service scope of existing hospitals, the author finds out the scope of hospitals that need to be supplemented, and then calculates the scope of service area after taking several random points within the scope, and finally finds the one with the largest service scope is the optimal location. The results obtained by optimizing the site selection can provide a scientific reference for the rational layout of medical facilities in Haikou City in the future.

As urbanisation continues, problems such as spatial congestion, population concentration and lack of environmental greenery are gradually coming to the fore. The existing urban green spaces have formed a scattered layout in the city due to the needs of the service recipients, their distribution, and other environmental factors making it difficult to balance functionality and economy, thus, creating an inability to achieve the maximum extent of green space service range requirements as well as forming an organic network system. This paper adopts a geographic information system (GIS) analysis model and attempts to use technical means to analyse pocket parks in cities, focusing on the spatial distribution of residents, their composition, demand differences, the competitiveness of neighbouring parks, service facilities and the degree of road radiation. The study also explores a digital analysis-based approach to the layout of urban pocket parks, which combines the needs of residents with the spatial layout of pocket parks in a practical way. This ensures the needs of residents and the balance of spatial distribution can be fully considered. In addition, the urban habitat and ecological environment can be improved and the overall appearance of the city can be enhanced.

With the rapid development of earth observation technology, remote sensing has become an important means of environmental monitoring. Remote sensing technology has the advantages of low cost, high efficiency and convenience, and has been widely used in the research of vegetation cover change in recent years. Based on the MODIS NDVI data from 2000 to 2019, this paper studied the spatial-temporal evolution of vegetation coverage in the Three-River Headwaters region by using the methods of maximum value synthesis, spatial trend analysis. The results showed that: 1) On the inter annual scale, NDVI in the Three-River Headwaters region showed an upward trend of fluctuation. In terms of spatial distribution pattern, it shows the overall distribution characteristics of "lower in northwest, higher in southeast." 2) In terms of spatial change trend, NDVI in most areas of the Three-River Headwaters region has been improved. However, in terms of dynamic persistence, the areas with uncertain future NDVI change trend account for more than 70%, and the areas with significant continuous improvement account for a relatively low proportion, which requires attention and attention.

The traditional mine remote sensing change monitoring requires human-computer interaction interpretation, and the timeliness cannot meet the demand. This paper studies the application of deep learning in remote sensing stope change monitoring in open pit mines, and analyzes the accuracy and applicability of automatic extraction results. Combined with information such as roads, mining rights, and urban zoning, the target area for open stope changes is provided. It provides a new technical idea for mine remote sensing monitoring.

Traditional village architectural landscape is rich in cultural values, but currently traditional village architectural landscape is facing a series of challenges of vulnerability issues. This paper identifies the factors influencing the vulnerability of traditional village architectural landscape using on-site research and expert consultation, analyzes the factors influencing the vulnerability of traditional village architectural landscape using an improved geographic modeling method named Adversarial Interpretive Structure Modeling method, and puts forward relevant suggestions. The conclusions show that among the factors influencing the vulnerability of traditional village architectural landscape, the root factors are building materials, topography and geology, urbanization rate and climate and hydrology, the intermediate factors are building structure, building age, building function, residents' cultural identity, policy concern, financial support, tourism development and natural disasters, and the direct factors are residents' willingness to retain, usage rate of residents and land use.

Taihu Lake, one of the five largest freshwater lakes in China, is located in the south of the Yangtze River Delta, with a water area of 2338.1km². In recent years, with the deterioration of water quality, the water pollution in Taihu Lake has become increasingly serious. In 2007, a large-scale outbreak of cyanobacteria caused a water crisis for nearly 2 million people in Wuxi. The main performance of water pollution is that the content of nitrogen and ammonia in water exceeds the standard, which leads to eutrophication of water and excessive proliferation of algae. Therefore, this paper uses Landsat-5 TM, Landsat-7 ETM and Landsat-8 OLI remote sensing data to study the information extraction methods of eutrophic polluted water bodies in Taihu region. By comparing with the health status report on Taihu Lake, it is found that the calculation results of remote sensing data can well reflect the distribution characteristics of ammonia nitrogen pollution in this area, and provide a basis for monitoring and controlling ammonia nitrogen pollution in this area by using remote sensing technology.

【Objective】In order to grasp the quantity of cultivated land in Yangzonghai basin, the temporal and spatial variation characteristics of cultivated land were analyzed, which could provide a reference basis for the sustainable utilization of cultivated land resources and land spatial planning in Yangzonghai basin. 【Method】Based on ArcGIS, Fragstats 4.2 and other software, the RS data from 2005, 2010, 2015 and 2020 were selected to analyze the dynamic variation and spatial distribution pattern of cultivated land in the study area using different methods such as dynamic variation degree, different terrain conditions, transition matrix, geo-informatic atlas, and landscape pattern index. 【Result】From 2005 to 2020, the number of cultivated lands in the study area showed a decreasing trend with a reduced area of 816.45hm² and a dynamic variation degree of -0.66%. From the county (district) scale, Yiliang County showed a continuous decline trend in the past 15 years, while the other three counties (districts) increased and decreased in each period; The topographic conditions had a certain impact on the cultivated land spatial distribution. The cultivated land in the study area occupies a large proportion in the cloudy aspect and semi-cloudy aspect with the altitude < 2300m and slope < 25°; From the perspective of geo-informatic atlas and landscape pattern index, cultivated land increased and decreased in each period from 2005 to 2020. The main types of land increasing are forest land, unused land and construction land, among which forest land accounted for a large proportion, indicating that the local ecological protection measures such as "returning farmland to forest" had achieved certain results. The landscape pattern index changed in each period from 2005 to 2020. Throughout the 15 years, the number of cultivated land patches had increased, and the average patch area and aggregation degree had decreased, indicating that the overall spatial aggregation of landscape pattern had weakened, the trend of landscape fragmentation had been presented. 【Conclusion】Based on the RS data of 2005, 2010, 2015 and 2020, the spatial distribution pattern changes of cultivated land in Yangzonghai basin in recent 15 years were analyzed from different angles by using the methods of dynamic variation degree, different topographic conditions, transfer matrix, geo-informatic atlas and landscape pattern index, so as to provide an important basis for rural revitalization and village planning in the future.

This study collected data related to ecological environment, society and economy in Yushu Tibetan Autonomous Prefecture from 2000 to 2015, and used ArcGIS 10.4.1 and Fragstats 4.2 software to analyze the ecological vulnerability of the area by using Principal Component Analysis (PCA). In the research process, construct a DPSIR-EES conceptual model that combines the Driver-Pressure-State-Impact-Respinse (DPSIR) model and Ecology-Economy-Social (EES), using Delphi and Analytic Hierarchy Process (AHP) analyze the changes in time and space of ecological security in Yushu area from 2000 to 2015. At the same time, questionnaire survey was used to analyze residents' cognition of ecological environment in Yushu area. Finally, the paper analyzes the changes and trends of ecological security in the "Ecology-Economy-Social" complex ecosystem formed on the basis of traditional production and lifestyle in Yushu. The results show that: 1) There are differences in the time and space dimensions of the ecological vulnerability of Yushu. In the time dimension, there is a weak trend toward both ends, and the spatial dimension shows a trend of high in the northwest, general in the middle, and low in the southeast. 2) There are differences in the spatial dimension of Yushu ecological security. The eastern region is significantly higher than the western region, showing the distribution characteristics of insecurity in the northwest, early warning in the central region, and security in the southeast. In the time dimension, the range of changes in each degree is less than 5%, and there is a bipolar development weak trend. 3) Residents in Yushu Prefecture whose main body is farmers and herdsmen have a better understanding of the overall situation of ecological security. 4) The natural climatic conditions and traditional production and lifestyles in Yushu area jointly affect the ecological security status. Ecological security conditions are poor in areas with harsh natural conditions and underdeveloped economies, while ecological security conditions in areas with relatively superior natural and economic conditions are relatively good.

With the further acceleration of China's urbanization process, atmospheric pollution is increasing, and haze occurs frequently throughout the country, exposing a series of urban planning problems in China's urban development, which pose new challenges and requirements for the future development of urban planning. In view of the direct causal relationship between haze and urban planning and management, we can put forward new planning ideas by exploring the relationship between the spatial distribution of urban planning and haze, so as to clarify the focus and development strategy of urban planning in the haze era. The application of modern geographic information technology can optimize the level of urban planning management, thus realizing the goal of modern urban planning. Based on the remote sensing data of Anshan City in 2016, this study divided the functional areas of the city, analyzed the air quality data of the study area for four consecutive months from November 2016 to March 2017, and analyzed the impact of the landscape ecological planning pattern of Anshan City on the spatial distribution of haze through GIS spatial analysis technology. The research shows that the urban landscape pattern of Anshan has a very important impact on the formation of haze. Future urban planning should be based on the implementation and management of urban spatial layout planning, energy planning, road system planning, green space system planning, transportation planning and wind corridor planning. This study provides a scientific basis for urban haze governance.

By combining the offsite interpretation and field verification and the remote sensing data in a natural reserve in Tibet in 2012, 2014, 2016, 2018 and 2020, this article investigates the situation of mine compaction damage and its restoration. Also, using the result of 2012 data as a benchmark, it is focused on analysing the dynamic changes over the following four survey years in terms of compaction damage and restoration, under mining and abandonment mines, and the types of minerals involved. Then, the chart of incremental changing trends is generated. This study reflects the real-time information storage function of satellite remote sensing monitoring technology and its efficiency and advantages in wide-area ecological and geological environment surveys. The results of this study have provided data and technical support for the future management of the mine geological environment in natural reserves in western provinces in China.

BDS-3 (Beidou System 3) reached global coverage in June 2020. In comparison with BDS-2 (Beidou system 2), BDS-3 has greatly improved in terms of satellite orbits, atomic clocks, and signal transmissions. BDS-3 is now being used widely in practice. This paper is focused on performance analysis of BDS-3 vs. BDS-2 and GPS. Six baselines of different lengths were selected to result in single-system multi-frequency relative positioning solutions from BDS-2, BDS-3, and GPS, respectively. The long baselines were resolved with using the random walk constraints on the residual atmospheric delay. The positioning performance of each system was analyzed against the atmospheric delay error and the convergence property of the floated ambiguity resolution. The experiments showed that the convergence speed of BDS-3 was twice as fast as BDS-2, and the average convergence speed was also faster than GPS, which was increased by about 2.7%. In comparison with BDS-2, the accuracy of BDS-3 was improved by about 40%, but the accuracy was 7% lower than GPS solution and the positioning stability of BDS-3 was also not as good as GPS.

This study assessed the ecological risk of urban landscape patterns, extracted and analyzed the remote sensing image information of the study area using RS technology, and determined the characteristics of the area's dynamic land use change. Then, using spatial analysis, overlay analysis, and spatial statistics in GIS, the landscape ecology statistics of the study area were conducted based on the annual land use changes in Xingtai City over the previous 20 years. The research shows that: (1) Land use change in Xingtai City from 2000 to 2020 indicates that farmland is the primary land source for urban expansion and development in the process of urbanization; (2) The degree of landscape fragmentation in the surrounding areas of cities and towns in the study area has decreased, and the degree of ecological risk in these areas has also been significantly reduced, reflecting that reasonable land planning can reduce the degree of ecological risk; (3) By analyzing the spatial distribution characteristics of landscape ecological risk, this paper identifies the crisscross zone of diverse ecological function areas as the region with the highest ecological risk.

Background: The geographic information monitoring and early warning system can effectively warn the relevant natural disaster information in advance and help rural areas reduce the loss of natural disasters. Existing research generally focuses on the technical research of geographic information monitoring and early warning system, which lacks a comprehensive analysis of the comprehensive benefits of disaster prevention and mitigation in rural areas. Methods: A comprehensive evaluation model of system dynamics was constructed, and the comprehensive benefit relationship between geographic information monitoring and early warning system and rural disaster prevention and mitigation was analyzed. Results: Through the construction of a multi-dimensional dual-element system dynamics model, it was found that the geographic information monitoring and early warning system can effectively help improve the comprehensive benefits of disaster prevention and mitigation in rural areas.

Based on Landsat8 OLI/TIRS data, this paper studies the interaction relationship between the heat island and influencing factors of the core area of the capital. Combined with comprehensive analysis of multi-source data and spatial data exploration, the spatial autocorrelation pattern and spatial correlation between the heat island and influencing factors in the core area of the capital are analyzed, agglomeration mode. The spatial heterogeneity of influencing factors and the interaction between factors were analyzed by using multi-scale geographic weighted regression model and geographic detector model, and the main influencing factors of heat island were detected. The study found that the spatial and temporal distribution of thermal environment in the core area of the capital has obvious spatial autocorrelation; the multi-scale geographically weighted regression model has high fitting accuracy and rich model interpretation information, and the model relaxes the broadband information of different factors. Geographic detector factor detection found that building density, night light and POI were the main influencing factors of the heat island in the core area of the capital, and the factor interaction analysis found that the single factor effect in the core area of the capital was more significant, and there was a weak interaction between the factors.

Taking the geographic information big data of single buildings data, based on the Geographical Information System (GIS), the spatial pattern and characteristics of buildings' distribution in Guangzhou’s urban district are analyzed by using the methods of average nearest neighbor distance analysis, kernel density estimation, and spatial autocorrelation analysis, and the calculation method of community building density is constructed. The results show that: (1) Geographic information big data is applicable as an important data source for studying the pattern and characteristics of intra-urban buildings; (2) The results of GIS-based research show that the distribution of single buildings in Guangzhou’s urban district is strongly clustered, with super-high-rise buildings having the highest agglomeration degree, and the building height showing multi-core planar agglomeration, hot spots are mainly distributed in the old area, the core area, and the east, south, and north of the urban district; (3) The GIS-based spatial statistical analysis shows that the building density has significant spatial autocorrelation, and the building density is higher in the old area, the north side of the Pearl River in the core area, the east side of the Pearl River in the urban district and the north side of the eastern Pearl River. Generally speaking, the spatial difference of building density in Guangzhou’s urban district is obvious. The building density in the west is higher than that in the east, and the central area is significantly higher than that in the edge area. The distribution pattern of buildings follows the natural geographical characteristics of "near mountains and rivers."

Plain regulating reservoirs have played an important role in optimizing the allocation of regional water resources, strengthening the conservation and protection of groundwater, and promoting the sustainable development of economy and society. By using reservoir safety monitoring index to implement the dynamic safety management during the initial operation period, it is helpful to find abnormalities in time and effectively control risks. Based on prototype monitoring results of a regulating reservoir in the initial five years, the temporal and spatial laws and characteristics of the project operation behavior evolution were summarized and refined. It was found the embankment dam was still in settlement and the settlement did not converge, and the seepage pressure of the dam foundation was significantly affected by the water level. By constructing the typical effect quantity sample which can reflect the effect of adverse load and statistical model, the typical small probability method, the confidence interval method and measured extreme value were comprehensively applied, then effective and practical safety monitoring indexes were established, which can be used to monitor reservoir safety operation and realize early warning and prediction.

Wuhan has experienced frequent floods in recent years as a result of global warming. In this paper, using the rainfall data of Wuhan meteorological station from 1960 to 2016, rainfall indicators such as annual rainfall, annual rainfall days and rainstorm days were selected to analyze the rainfall characteristics and change trends in Wuhan city in the past 60 years. The study shows that the annual rainfall in Wuhan shows a trend of significant increase, while the annual number of rainfall days is decreasing significantly. Rainfall is primarily concentrated from April to August, with June and July accounting for 25.9% and 27.4% of the year, respectively, in terms of rainstorm days. The annual rainstorm days, annual maximum 1-day rainfall, maximum 3-day rainfall, maximum 5-day rainfall, maximum 7-day rainfall and maximum 15-day rainfall all show an increasing trend, indicating an increased risk of extreme rainfall in Wuhan.

Uneven distribution of water resources and supply and demand have gradually become prominent problems in the process of China's economic and social development. This paper takes the research object of X coal mine, based on the actual mine water inflow and mine water demand from 2013-2022, predicts the water inflow and water demand of the mining area from 2022-2026, establishes a multi-objective optimal configuration model, achieves the goal of mine quality water supply, provides a reference scheme for the optimal allocation of mine water resources to maximize the optimal water resources allocation, and guides the treatment and utilization of mine water resources.

Aiming at the problems of low efficiency and untimely monitoring of traditional monitoring of transmission line tower slopes, this paper uses Beidou static relative positioning technology to conduct monitoring research on transmission line tower slopes. Taking the carrier phase observation value or the phase measurement pseudorange as the basic observation value, through continuous observation, sufficient observation data is obtained, and after adjustment calculation, the positioning accuracy of transmission tower slope monitoring is improved. Finally, a transmission tower slope monitoring platform is built, the slope monitoring data is processed by the Laida criterion, and the reliable monitoring data is obtained by using the baseline vector solution, and the slope stability is analyzed according to the data, feasibility and reliability of the monitoring platform.

The study of forest carbon storage and carbon density has become the focus of the study of forest carbon source (sink). Three typical subtropical evergreen broad-leaved forest in Jiangxi Province were taken as the research object, which included Cyclobalanopsis glauca forest, Schima superba forest, and Castanopsis eyrei forest. Combined the investigation method with remote sensing estimation, the related factors with carbon storage sample plot of Cyclobalanopsis glauca forest, Schima superba forest and Castanopsis eyrei forest had surveyed in Jiulian Mountain nature reserves, Wuyi Mountain nature reserves and Dagang Mountain nature reserves. In this paper, a remote sensing estimation model of vegetation index was established. The carbon storage and carbon density of subtropical evergreen broad-leaved forest in Jiangxi province had been estimated, and the sequential variation had been analyzed in latest 30 years. The result showed: in the subtropical evergreen broad-leaved forest vegetation layer, different organs had different carbon content; three kinds of subtropical evergreen broad-leaved forest carbon storage and density from high to low was Castanopsis eyrei forest, Schima superba forest, Cyclobalanopsis glauca forest. The vegetation biomass NDVI model was y=2.6977e^2.5523x. From 1985 to 2015, the carbon storage of vegetation had increased from 0.2401 GtC to 0.3664 GtC, the carbon density of vegetation had increased from 116.9 t/hm² to 133.65 t/hm².

For Magnitude 7.4 large-scale earthquake that occurred in Maduo County, Guoluo Prefecture, Qinghai Province in 2021, two Sentinel-1a images were used for orbit descent, one Sentinel-1a image and one Sentinel-1b image were spliced and cut, and D-InSAR technology was used for analysis. Despite extensive research into this earthquake, understanding its causes was biased due to its low coherence characteristics. In this study, we derived seismic LOS direction interferograms and coherence maps from Sentinel-1A/B data. The interferogram shows that the fault has multiple asperities and the deformation is larger as it is closer to the epicenter. According to the distribution map of the fault zone, it is concluded that the seismogenic fault is the fault zone of the southern margin of Gande. The stress analysis of the plate where the earthquake is located shows that the probability of earthquakes in the south of the Bayan Har block where the earthquake is located will increase in the future.

Typhoon Siamba made landfall in western Guangdong on July 2, 2022, causing great losses to crops in western Guangdong. Radar remote sensing can penetrate through clouds and fog and is suitable for identifying flooded areas before or after typhoons and in rainy weather. However, radar flooded waterbody mapping faces two major problems: distinguishing between flooded areas and natural waterbody, and the other is noise interference from confusing ground objects. Aiming at these problems, the study proposes a method of high-precision cropland information combined with waterbody identification. Based on GF-3 and Mapbox data, this paper first uses watershed semantic segmentation to extract initial waterbody, then uses SegFormer deep learning technology to identify cropland, and finally realizes flooded cropland mapping based on cropland information. This study concluded that the affected cropland in Zhanjiang and Maoming City, Guangdong Province, China is 75.437 km² and 31.175 km² respectively. The cropland extraction accuracy and Intersection over Union (IoU) are 96.65% and 92.64% respectively. The study shows that flood monitoring combined with cropland identification information can effectively avoid noise interference and accurately extract the flood range, and achieve high-precision flooded cropland mapping.

The identification of peeling off blocks is an important part of tunnel disease detection. In order to effectively solve the problem of accuracy of the identification of peeling off blocks in tunnels, this paper focuses on the use of the three-dimensional coordinates of the point cloud data obtained by the laser scanning system to realize the detection of peeling off blocks. A study of automatic detection of blocks. In the proposed automatic detection method for peeling off blocks, the original tunnel point cloud data is first filtered to remove outliers; then, the point cloud data is coordinately transformed to convert the cylindrical point cloud into a rectangular point cloud; the cloud constructs a triangular mesh, and by calculating the area of the triangular mesh and the projected area of the triangular mesh, the specific position of the peeling block can be obtained; finally, the point cloud is converted into an image, and the Hough transform is used to mark the pipeline and tunnel joints and get more intuitive detection results. This paper uses the real tunnel point cloud data to carry out the peeling off block detection experiment, and the results show the effectiveness and feasibility of the method.

The parts of multi-granularity spatio-temporal objects is to describe the "whole-part" relationship between different objects in the pan-spatial information system. It is the key to describe the internal parts, dynamic changes and classification system of spatio-temporal objects, and it is also an important part of octet description objects in the pan-spatial information system. The traditional relational description models of spatio-temporal objects have some shortcomings in complex relationship, semantic relevance and scene dynamic expansion process. This paper intends to elaborate the basic concept, classification method and description framework of the "whole-part" relationship of multigranularity spatio-temporal objects based on mereology logic. The application scenarios, life cycle and the "whole-part" relationship of multi granularity spatio-temporal objects are tested on Cesium platform. The experimental results show that this model can effectively make up for the deficiency of the current "whole-part" relationship expression model, which provides the corresponding theoretical basis and data organization mode for multi-granularity spatio-temporal modeling of pan-spatial information application scenarios.

Accurately predicting coal seam thickness is of great significance for mine production safety. Based on seismic exploration and geological data, seismic attribute analysis technology and logging constraint inversion technology were used to analyze the variation law of coal seam thickness in the study area. The prediction results show that for the study area with complex geological conditions, the seismic comprehensive interpretation technology can effectively predict the trend of coal seam thickness, and the sedimentary environment in the study area affects the regional variation of coal seam thickness.

Spatial and temporal resolution of satellite precipitation products is high but they have errors in geographic distribution and data accuracy, while rain gauge data have low resolution but high accuracy. Therefore, the two precipitation observation data can be fused to obtain high accuracy and high resolution precipitation products. In this paper, we propose a data fusion method based on image registration and warping in image processing for data correction of satellite precipitation products. The essential element is to construct a cost function containing a term constrained on the precipitation field differences and a term constrained on the mapping domain. The warping vector field is obtained by minimizing the cost function and applied to satellite precipitation products for the purpose of position correction. The quantitative precipitation estimate (QPE) of FY-4A is corrected by rain gauge station data in position for extreme precipitation in Henan Province in July 2021. The results show that the QPE distribution has obvious shape and position error, but after position correction, the mean absolute error, root mean square error, and position error of the QPE are reduced, while the correlation coefficient and the fit to the station improved. However, since the registration algorithm is actually a process of finding large-scale feature matching in image processing, the iterative process will be difficult to converge due to the widely varying precipitation fields.

Due to climate warming and increased precipitation, the permafrost of the Tibet Plateau (TP) has undergone serious degradation along with obvious lake expansion in recent decades. Model simulation is often used to analyze the contribution of permafrost melting to lake expansion, which may have many limitations. Taking Hohxil Lake (HL) basin over north TP as an example, this study makes full use of Sentinel-1 images by an improved small baseline subset interferometric technique (SBAS-InSAR), monitors the permafrost deformation from 2015 to 2020, and estimates its contribution to the lake expansion. The results show that the permafrost settlements mainly occur in the flat terrain around HL. The average line of sight (LOS) de-formation rate of permafrost is -3.59 ± 0.001 cm/yr, where there existed many obvious funnel-shaped thawing areas around the lake, indicating a close relationship between lake expansion and permafrost under-ground ice melting. The long-term linear deformation rate of underground ice is inverted by the traditional linear model, and the melting rate is estimated to be (31.17 ± 0.0054) ×10⁶ m³/yr with 9.3% contribution to the HL expansion. This study takes full advantage of Interferometric Synthetic Aperture Radar (InSAR) to quantitatively analyze the contribution of permafrost to lake expansion, which provides a new insight into the study of permafrost hydrological process and the proposed method can be easily extended to analyze lake water budget for underground ice in other watershed over the TP.

Aiming at the problem that the traditional viaduct deformation monitoring has high accuracy but long monitoring period and consumes a lot of manpower and material resources, it is difficult to extract the severe deformation area of viaduct in time. In this paper, based on the small baseline set interferometric synthetic aperture radar (SBAS-InSAR) technology, the deformation information of viaducts and surrounding areas is retrieved, and the severe deformation areas of urban viaducts and surrounding areas are extracted. Taking three viaducts with large traffic flow in Hohhot as the research object, the deformation results of the study area from August 2020 to September 2021 were obtained. The deformation causes are analyzed combined with the inversion results. The results show that there are five large deformation areas in the three viaducts, and the main deformation causes include soil erosion or urban waterlogging caused by rainfall, surface construction and rail transit operation. The research shows that this method can accurately extract the severe deformation area of urban viaducts, and provide a reference for analyzing the causes of viaduct deformation.

Continuously Operating Reference Station (CORS) is an integrated service system constructed based on multi-base station network Real Time Kinematic (RTK) technology. CORS system is composed of several fixed and continuously operating GNSS reference stations which form a network automatically providing different types of GNSS observations such as carrier phase and pseudorange, various correction numbers, status information and other GNSS services through computers, data communication and Internet (LAN/WAN) technology. This article selects an appropriate location and sets up a reference station to receive Beidou, GPS, GLONASS satellite data basing on CORS system, establishes an All-Terrain inspection field, designs a set of mobile positioning terminals, and develops a set of real time inspection software for the calibration of navigation products. Experimental verification results shows that the calibration system designed by this article is applicable to calibrate navigation products and worthy of extensive promotion.

Taking Hongshan District, Wuhan City as the research area, using remote sensing data, POI data and Baidu heat map data, we used kernel density and two-factor mapping method to analyse the coupling degree of public service facilities and population in space and time. The remote sensing map and the population heat density from the distribution, there are differences, using multiple sources of data for validation can compensate, the shortcomings caused by a single data source. It is also possible to analyse the coupling between public service facilities and population distribution from two latitudes in time and space. Taking the Hongshan District as an example, we can conclude that: 1. Spatially, public service facilities in Hongshan District show a pattern of one main centre and two to three secondary centres, with a relatively uneven spatial distribution; 2 .Spatially, the population is coupled with public service facilities in a primary and secondary manner, with better coupling concentrated in the areas south of Hongshan Square and Liyuan Street area; 3. From the analysis of the population heat map, the demand for transportation facilities is greater on weekdays than on weekends, and the demand for shopping facilities is greater on weekends than on weekdays.

In order to improve the prediction accuracy of the slope deformation prediction method in mining area, aiming at the problems of many disaster causing factors of slope deformation and BP neural network is easy to fall into local minimum value, a prediction method based on the combination of genetic algorithm and BP neural network based on grey correlation analysis is proposed. The grey correlation analysis method is used to screen the main influencing factors, and the factors with high correlation degree are used as input indexes to simplify the network structure. Then, the genetic algorithm is used to optimize the BP neural network to establish the GA-BP model, and finally the prediction is compared. The results show that the grey correlation analysis can further improve the consistency between the predicted value and the real value, and the model can accurately predict the slope deformation. The research results have important auxiliary reference significance for mine safety production.

The assessment of the importance of biodiversity maintenance functions is a significant content in the delineation of regional ecological protection red lines of China. However, estimation in this respect is less in previous studies involved in Chuxiong, an important ecological conservation area in Yunnan Province. Therefore, this study provides a quantitative estimation of the functional importance of biodiversity maintenance in Chuxiong with the Net Primary Productivity model. Then, the spatial distribution pattern of the importance of biodiversity maintenance functions was analyzed after quantile classification, and the distribution characteristics of importance levels in different land use types were explored with land use classification data. The results show that: (1) The importance of biodiversity maintenance function in Chuxiong shows a decreasing trend from the north and east to the west and south. (2) Lufeng, Wuding, and Yongren have a relatively high proportion of areas with extremely important biodiversity maintenance functions, while Chuxiong City and Shuangbai are mainly important districts. In addition, Dayao and Nanhua are mainly distributed in general important areas. (3) The important and extremely important areas of biological diversity in Chuxiong are mainly distributed in grassland, forest land and other land use types. The research results can provide a scientific basis for the delimitation of Chuxiong state nature reserves and ecological corridors, and are of great significance to biodiversity conservation.

Acquiring the land use change information in Zhengzhou quickly and accurately can provide a scientific basis for the overall planning of land resources, ecological environmental protection and sustainable social and economic development in Zhengzhou. This paper takes Zhengzhou City as the research area, is supported by the Google Earth Engine (GEE) cloud platform, and uses the 2000-2020 Landsat-5 and Landsat-8 images as the remote sensing data source. In this paper, the random forest algorithm that combines spectral, textured, and topographic features is used for rapid monitoring of land use types. The research results show that: 1) The random forest algorithm based on GEE has good classification accuracy, the overall accuracy of the classification results in each target year is above 0.90, and the Kappa coefficient is above 0.86. 2) In the past 20 years, the land use in Zhengzhou has undergone profound changes. The most important feature is that the construction land has been continuously expanded from the city center to the outside, and has gradually evolved into a closely connected integration.

With the rapid development of the modern transportation network, the phenomenon of roads crossing the Great Wall is increasing day by day, making ground traffic vibration an important factor affecting the safety of the Great Wall. This paper firstly analyzes the correlation between traffic flow and the vibration acceleration of enemy stations on the Great Wall; then establishes a vibration data denoising method based on variational mode decomposition (VMD) combined with FLANDRIN criterion, and removes the high-frequency noise of vibration acceleration; To denoise the acceleration data, an integrated VMD and Hilbert-Huang transform (HHT) time-frequency feature extraction model was introduced to extract the instantaneous vibration frequency and intensity. The results show that the ground traffic vibration has a great influence on the short-cut Great Wall, which leads to the vibration of the enemy stations on the Great Wall, and the vibration frequency is 0.27 Hz; the VMD-HHT model can accurately obtain the instantaneous vibration frequency and intensity characteristics of the enemy stations on the Great Wall. This research can provide an important reference for the real-time safety monitoring and protection of the Great Wall.

In this paper, using data information from several gravity measurement points with significant variation in Shandong Province, combined with fieldwork and mathematical model calculations, the scientific conclusion that the change of gravity increases when filling or adding buildings near the measurement point and decreases when excavating near the measurement point is drawn, i.e., the addition or absence of mass around the measurement point has a certain influence on the regional gravity field. At the same time, the mathematical model calculation due to the taken density and other media parameters are only regional averages, the model calculated value and the actual mass increase or decrease caused by the gravity change has a certain deviation, can only roughly estimate its effect on the gravity change of the measurement point, but the study can make the gravity measurement value becomes relatively more reliable, and has practical significance for the in-depth analysis of the change of gravity observation data.

Inspection and complete demonstration of underwater conditions are of utmost importance to the safe operation of large-sized structures. At present, however, there is scarcely any research on key techniques such as the multi-source data integration of underwater inspection, underwater three-dimensional visualization, etc. Through the R&D of multi-source data processing and visualization system of underwater inspection, and by collecting actual testing data of underwater inspection of the Hong Kong-Zhuhai-Macao Bridge, the authors of this study verified big-data integration of the multisource observation of the underwater environment and visualization demonstration capacities of underwater three-dimensional sceneries of the Hong Kong-Zhuhai-Macao Bridge. The research results of this study could effectively support the monitoring and management of the underwater environment of large-sized structures.

In order to improve the reusability of aero-geophysical data in multi-platforms, we take in depth studies of data storage and symbolize principle in aero-geophysical database. In this paper we carry out symbol management and symbolize method in different working platforms. Then, we build the combination between different platforms by parameter configuration and using C# development language to carry out a new software in .NetFramework 4.0, and make it a plug-in for aero-geophysical data management software Geoprobe Mager. The aero-geophysical data-symbolize software is designed to complete data symbolization according to user requirements. In practice, we also achieve the aims that to simplify the workflow and data reusement.

Forest biomass is an important ecological parameter for forest research, which is greatly important to the understanding and analysis of global carbon cycle. Landsat Time Series (LTS) data, as medium optical resolution data, have the merits of long time–span and appropriate spatial resolution. Forest biomass research based on this data can better guide human understanding of forest ecosystem. In this study, LTS data and sample biomass data were used to study the forest biomass modeling. This study explored the effects of forest biomass modeling based on features extracted from two LTS processing methods and three algorithms including random forest (RF), extremely randomized trees (ERT), and eXtreme Gradient Boosting (XGBoost). Among these models, the accuracy of the models based on the features extracted by Best Available Pixel (BAP) image composite and three algorithms is generally higher. Among the models constructed by combining feature groups extracted from BAP image composite and three algorithms, the models constructed by ERT were stable in most situations from the perspective of coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and bias. However, the XGBoost model that combines all variables performed best in perspective of R² and RMSE, where R² can reach 0.87, and the RMSE, MAE, and bias were 34.84 Mg/ha, 19.26 Mg/ha, and 2.29 Mg/ha, respectively. The textures and Tasseled Cap (TC) indices also show favorable performances in different models in the feature importance analysis.

Through the combination of various geophysical methods and high precision measurements with geological survey results, the structural development of expressway tunnel section is inferred, and a number of faults and joint fracture zones are delineated. A variety of geophysical exploration methods including controlled source audio magnetotelluric (CSAMT) and magnetic exploration are used to explore tunnels in igneous rocks. According to the morphology and electrical distribution characteristics of resistivity anomalies and magnetic prospecting anomalies, several examples are listed and discussed. The stability of overburden in tunnel area is poor, which provides corresponding suggestions for the construction side in expressway construction to ensure the safety of construction.

Mining coal mines can cause large scale and extensive surface subsidence in mining areas. It not only affects the economic development of the mine, but also poses a threat to the surrounding environment and the safety of people's lives. Therefore, it is very important to carry out long time surface monitoring in mining area. For D-InSAR (Differential Interferometric Synthetic Aperture Radar) technology is vulnerable to the phenomenon of unstable monitoring results caused by temporal and spatial decoherence and atmospheric delays, this study uses the StaMPS (Stanford Method for Persistent Scatterers) technology for time-series subsidence monitoring. 22 Sentinel-1A images from June 15, 2019 to December 6, 2020 were used to monitor the subsidence of Zhangshuanglou Coal Mine. The results show that: During the monitoring period, there are three obvious subsidence funnels in Zhangshuanglou Coal Mine. The study area is sinking almost all the time with only a brief rebound. The maximum displacement velocity reached -63.9 mm/yr and maximum cumulative displacement value reached -95.8 mm. Moreover, the subsidence value and velocity are almost inversely proportional to the distance to the center of the funnel. This proves that StaMPS technology and Sentinel-1A data can be used to monitor surface subsidence in mining areas and provide a basis for the study of the subsidence pattern and causes of the target area.

The classification and extraction of lithology is an important research direction of remote sensing geological survey, but lithology is different from other objects, often because of terrain differences, natural weathering and other factors, spectral differences are not obvious, simple unsupervised classification may not have good results even if the number of iterations increases. For Landsat8 data, we adopt two common data dimension reduction methods, classify the results by K-means unsupervised classification to divide lithology, compare the results with the dimension reduction, and obtain good experimental results, which can be used as a good method to quickly understand the geological situation of the study area.

Aiming at the problem that the single-axis rotation modulation technology is currently used in gravity measurement, only considering the inertial device's zero bias will restrict gravity measurement accuracy, a single-axis rotation modulation strapdown gravity vector measurement method considering the scale factor error is proposed. This method analyzes the one-way continuous rotation scheme and finds that the up-direction scale factor error stimulates additional attitude errors. To suppress the error and improve the measurement accuracy of the strapdown gravity vector, a dual-position forward and reverse turn-and-stop scheme is adopted, which designs a reasonable rotation path, stop position, and time. The simulation results show that the dual-position forward and reverse turn-and-stop scheme, considering the scale factor, can effectively improve the accuracy of gravity measurement compared with the one-way continuous rotation scheme. This study has great technical reference significance and engineering practice significance and can meet the needs of high-precision airborne and marine gravity measurement.

"Social perception technology" is a useful tool for analyzing the geographical and temporal aspects of people's behavioral responses during earthquakes. Using the magnitude 7.0 earthquake in Jiuzhaigou, Sichuan, China on August 9, 2017 as an example, this paper uses the method of combining emotion dictionary and rules to mine the social media data released by the disaster area 24 hours after the earthquake and analyze the earthquake microblog data, emotional polarity, and spatiotemporal characteristics. The number of active microblogs in this earthquake is highly related to population density, lifeline damage degree, epicenter distance and intensity, and the spatial distribution is imbalanced, according to the findings. The positive attitude in the disaster area outnumbers the bad mood. Population density, housing seismic performance, local people's understanding of earthquake avoidance and catastrophe mitigation, and popularization of earthquake scientific information are all key factors.

Urban public open space plays an important role in ensuring residents' daily leisure activities and improving the quality of urban ecological environment. Therefore, the reasonable and scientific evaluation of its recreational services can promote sustainable urban development. This paper selects Dongguan as a case area, and establishes an evaluation index system for public open space recreational services using GIS from three aspects, including service satisfaction, service capability, and supply-demand balance. The results show that the performance of recreational services in the towns of Dongguan city is related to the distribution of green space and population density. High-performing towns are located in the central area of Dongguan, and the towns with medium performance are mainly distributed around the central area. The towns with lower performance are distributed in the northeastern and northwestern fringe areas of Dongguan. To meet the needs of recreational services, it is recommended to prioritize the construction of pocket parks and street green space parks.

Urban transportation is an essential part of modern urban development. With the rapid development of China's economy, the number of private vehicles is increasing year by year, and traffic congestion is becoming more and more frequent. However, as existing map software determines the congestion judgment method based on the number of navigation software installed in users' vehicles or cell phones, there is a defect of inaccurate prediction results. This paper compares and analyzes the object detection methods based on deep learning by studying and analyzing the current mainstream object detection frameworks and finally selects YOLOv3 as the object detection tool. We combine the driving recorder video and GPS navigation tracking to judge the congestion situation in real-time. It feeds the congestion status to the map, effectively making up for the shortcomings of mapping software in judging congestion, facilitating car owners and traffic management departments to make more good travel planning and traffic diversions, and improving traffic efficiency.

The campus air environment directly affects the physical and mental health of teachers and students. In order to provide a quick and accurate way of monitoring campus air environment, this paper develop a real-time monitoring air environment detector based on Microcontroller Unit (MCU) STM32 and sensor. The monitoring system can detect dust particles PM2.5, harmful gas carbon monoxide (CO) concentration, temperature, humidity and illumination, display the real-time detected data on organic light-emitting diode (OLED) display screen, and transmit the data to WeChat applet for real-time viewing. The results show that the air environment detector designed in this paper can accurately detect the air environment in Sanshui campus of Guangzhou College of Technology and Business, providing effective protection for the healthy living environment of teachers and students.

As the drought continues to worsen, the water level at Lake Mead, which is the largest reservoir in the Colorado River basin, has fallen to an all-time low. And its water level has fallen faster than experts had predicted. The models predict that if the drought continues, Lake Mead's water level will drop to less than 1000 feet by the middle of the century. If the Wastewater Recovery Program (WRP) is implemented, the drought trend in Lake Mead would be greatly alleviated. From the models prediction, the water level after WRP reach 1083.43 in 2050, which is greatly larger than the original value (985.86) without WRP.

The urban heat island effect is the result of the rapid development of urbanization, and modern life creates huge amounts of heat. Land surface temperature is a key parameter to measure the water and heat balance of the Earth's surface and an important index to reflect the urban heat island effect. Retrieving land surface temperature by remote sensing technology is a commonly used method at present. Remote sensing monitoring method is the biggest advantage of the continuity of data acquisition, integrity, and real time, so as to make up and overcome the shortcomings of traditional methods, and can reveal the spatial distribution of urban heat island from the macro and change rule, but also from the micro level to reveal spatial and temporal variation characteristics of heat island effect, thus to alleviate provide more scientific conclusion on the basis of the urban heat island effect. This paper uses remote sensing technology to retrieve the land surface temperature in Shapingba District of Chongqing in recent 20 years. Analyzing the land surface temperature data and comparing it with the data of land use type change in this area provides the basis for the future urban construction planning in this area.

As the third largest fresh lake in China, the eutrophication problem of Taihu Lake is particularly prominent, and it is important to monitor the water quality parameters of Taihu Lake by remote sensing monitoring measures. Chlorophyll concentration is an important indicator parameter of eutrophication. The coastal zone imager (CZI) of China's HY-1C satellite has the advantage of high spatial and temporal resolution. In this paper, based on the band ratio model and HY-1C satellite CZI data, the chlorophyll concentration of Taihu Lake in each month of 2021 is inversed. The missing value and the influence of singular value are filled by data screening and fusion averaging so that the data quality can be improved. The analysis of the spatial and temporal variation of chlorophyll shows that: the overall Taihu Lake chlorophyll concentration in 2021 was lower than in previous years, with the highest value of 19.0 μg/L; the average chlorophyll concentration in winter was high, about 13.0 μg/L, while the average chlorophyll concentration in summer was low, about 4.0 μg/L, due to the influence of long-term precipitation; the overall central and western regions of Taihu Lake had higher chlorophyll concentration, the southern region also displays high values in winter while the chlorophyll concentration is low in the northeastern region for a long term. This study provides reference values for water quality monitoring and governance in Taihu Lake.

Map information measurement plays a very important role in map information transmission. It is an important prerequisite for evaluating cartographic quality, guiding map generalization and realizing map information transmission process, while fractal dimension is a measurement method for fractal spatial effectiveness. At present, there are few studies on the fractal dimension of map surface objects, and the area index cannot be used as a standard for measuring spatial information. The commonly used fractal dimension calculation methods have certain limitations. Based on the existing buffer method to calculate the fractal dimension of line objects, this paper proposes a buffer method to calculate the fractal dimension of the map surface objects, the correspondence between buffer radius and the area of surface objects is constructed to overcome the shortcomings of the existing methods. Experiments are verified and compared with the calculation results of the box counting method, which proves that the method in this paper is closer to the real value.

Gravity data processing, inversion and interpretation methods have had many problems for a long time, and probabilistic imaging technology can solve complex geological problems in gravity exploration, and the method is simple, relatively easy to implement, fast in operation, and relatively stable and accurate in calculation and interpretation results. In some areas, there is no need for many conditional parameter constraints, and it is not necessary to give the initial model and the specific process of fitting iteration in the gravity inversion. It only needs to scan the correlation function in the sliding window to calculate point by point, and many problems can be solved in large-scale data imaging inversion. In this paper, the author studies Bouguer anomalies and gradient imaging based on potential field separation, and gives the imaging results of various combinations of spherical models. The imaging method can display the spatial distribution of abnormal geological bodies and improve the lateral and vertical resolution of gravity data.

Population is the most active factor in urban development, and GIS can analyze and visualize the spatial characteristics of population distribution within a city. Based on mobile phone signaling data, this paper uses three methods in geographic information system analysis software (ArcGIS) to analyze the spatial characteristics of population distribution in the main urban area of Kunming by using three methods: kernel density analysis, trend surface analysis and spatial autocorrelation analysis. The results show that: 1) the combination of mobile phone signaling data and geographic information system can more accurately and conveniently study the spatial distribution characteristics of residential population in cities; 2) The overall spatial distribution of the residential population in the main urban area of Kunming is uneven, with the central area (Third Ring District) being significantly concentrated and the peripheral (new urban area) relatively scattered.

Vegetation is one of the important land covers and plays an important role in the ecosystem and climate change. Most of the current studies on vegetation and atmospheric change are based on the material and energy exchange between vegetation canopy and atmosphere. The study of the interaction between individual vegetation and atmosphere is affected by the limitations of acquisition. The development of the convolutional neural network derives a new approach for obtaining clump trees from PhenoCam. In this paper, a MaskRCNN method is proposed to train the model from PhenoCam images. Image enhancement processing is added to improve the training accuracy of the model. Then the training model is used to instance segment a clump tree from an individual PhoneCam imagery. The result of MaskRCNN instance segmentation mAP (mean average precision) can reach 0.887. In the vegetation boundary area and complex vegetation types, the effect of MaskRCNN segmentation is better than other methods. With the increase of image enhancement processing, the training accuracy of the model and the robustness of the data are enhanced. This study provides a feasible and efficient acquisition method for the clump tree segmentation.

In AutoCAD-based survey production process, it is usually necessary to overlay remote sensing images to carry out multi-source data fusion applications, and traditional methods are too cumbersome, low automation, and lack data security and sharing to effectively meet demand. In order to solve this problem, this paper proposes a novel remote sensing image dynamic superimposed method in AutoCAD, which will comply with the OGC standard network map tile service as an image data source. According to the view changes, depending on the user interaction, the level and range of image are calculated in real time and desired image tiles are requested from network, and these tiles are drawn with the overrule mechanism of AutoCAD. Applications in the actual survey project in Chongqing show that this method provides an efficient and feasible solution for overlaying remote sensing image data in AutoCAD.

Multi-source remote sensing image fusion is a data processing technology that complements two or more remote sensing images taken from the same target of multiple sensors to obtain more accurate and perfect comprehensive images. Image fusion is not only an important part of remote sensing detection data processing, but also has a wide range of applications in environmental detection, precision agriculture, urban planning and other fields. Based on the method of component substitution, this paper discusses the image fusion methods of Brovey transform, IHS transformation, PCA transformation and weighted fusion, and performs fusion experiments and analyses on four methods, and finally summarizes and makes prospects.

With the development of remote sensing technology, remote sensing images of buildings are of great significance in urban planning, disaster response, and other directions. When we use a neural network containing batch normalization layers for semantic segmentation, the neural network is sensitive to batch size and has low segmentation accuracy for occluded and dense buildings. This paper proposes a method for building segmentation in remote sensing images based on Nested UNet (UNet++) deep neural network. First, the UNet++ network is used to extract features, and the Group Normalization (GN) method is used instead of Batch Normalization (BN) to alleviate the model's sensitivity to batch size. Then, the weighted combination of Cross-Entropy Loss (CELoss) and DiceLoss is used as the loss function to improve the feature extraction ability of the neural network for unbalanced buildings. Finally, experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet++-GN) improves Mean Intersection over Union (MIoU) and Mean Pixel Accuracy (Macc) by 12.16% and 2.92%, respectively, compared with the original model (UNet++-BN).

Lodging is a major yield-limiting factor in rice. Accurate assessment of rice lodging is essential for yield damage estimation, agricultural insurance claims settlement and subsequent management decisions. This study aims to explore the effect of lodging on backscatter/coherence and spectral reflectance derived from Sentinel-1 and Sentinel-2 data. Based on this, an extraction method of lodging rice distribution using multi-source remote sensing data was proposed. The results showed that: (a) The lodging area of rice could be effectively extracted from dual-polarization Sentinel-1 image data with an accuracy of 87.87%; (b) The vegetation indices (NDVI, DVI and LSWI) extracted by Sentinel-2 are sensitive to lodging rice. Based on a certain threshold, lodging rice can be effectively extracted with an accuracy of 87.5%. Our findings demonstrate the potential of Sentinel data for near real-time detection of the rice lodging.

The 3D (three-dimensional) modeling has become an important digital and information technology with the continuous progress of science and technology, and it has been widely used in the ore prospecting, especially the uranium ore, which is an important strategic resource for the development of our country's nuclear industry. In recent years, 3D digital modeling technology has become a mainstream method in the geological industry with the rapid development of prospecting and prediction methods, and it plays a huge role in prospecting and prediction research for uranium deposits. In order to strengthen the prospecting prediction research of uranium deposits in the Xiangshan uranium ore field, a three-dimensional digital modeling study was carried out in this paper. Based on previous research of the metallogenic geological background, ore deposit characteristics and metallogenic regularity of the Julong'an uranium deposit in Xiangshan area, Jiangxi Province, a semi-automatic 3D digital and information modeling software, SKUA-GOCAD, was used to build topographic surface, structure stratigraphy and ore body models of the Julong'an uranium deposit in this paper. It can analyze the spatial distribution and geological morphological characteristics of each geological body in the study area using these digital and information models intuitively. Together with metallogenic characteristics, the metallogenic prospective area was divided using the 3D digital geological prediction model, and it showed that the prospecting potential for the Julong'an uranium mining area was great. From this research we can draw a conclusion that it has an obvious ore-controlling effect for the stratigraphy, structure and abrupt changing zone of interfaces of different formations. Meanwhile, the intersection region with the interface between the rock formations, the abrupt changing zone of the interface between different formations, and the intersection region of three interfaces are the favorable metallogenic areas. This research not only provided important prospecting ideas for the subsequent mineral exploration of uranium deposits, but also was beneficial to the next stage of ore prediction research in the Xiangshan area.

With the rapid development of computer technology, the requirements of geospatial data are also increasing, and the traditional spatial data measurement methods are then unable to meet the needs of information technology. The emergence of 3D laser scanning technology, with its unique advantages, has been widely used in the collection and processing of information data in various industries, but at the same time, the data obtained by 3D laser scanning technology is a huge amount of point cloud data, and the huge amount of data brings difficulties to the computer storage and query. Therefore, this paper organizes and manages the point cloud data through KD tree, and the experiment proves that KD tree can manage the point cloud data efficiently, and has high efficiency when performing the related radius search and nearest neighbor search.

The ocean front is closely related to the hydrologic environment and geographical characteristics of the ocean. To accurately detect and locate the ocean front, the ray acoustic model is used to analyze the physical characteristics of the ocean front, aiming to explore the application prospect of ray acoustic in ocean front telemetry. The simulation results show that under the influence of the ocean front, in the frontal sea area transitioning from a warm water area to a cold water area, the sound will deflect downward in advance, increase the contact times with the seafloor, and increase the grazing angle when contacting the seafloor. In the frontal sea area transitioning from the cold to the warm water area, the sound line will be deflected upward in advance, increasing the contact times with the sea surface and decreasing the grazing angle when contacting the sea bottom. With the decrease of ocean front gradient, the span of the sound line will increase gradually, the frequency of contact with the seabed will be reduced, and the grazing angle will be reduced. As the tilt distance of the ocean front increases, the sound lines behind the front become denser, and the grazing angle of the sound line and the seafloor gradually increases.

A method of obtaining tree height by integrating airborne LiDAR and optical data was proposed to improve the accuracy of tree height extraction by LiDAR. To remove the influence of ground fluctuation, the denoised and filtered point cloud data is first normalized by elevation, and then the tree height is retrieved using individual tree segmentation. Then, from the optical remote sensing image, the object-oriented multi-scale segmentation technique is used to extract the boundary of an individual tree crown, and the normalized point cloud data is used to segment the single tree, yielding the position and height information of the individual tree. The R² of the tree height derived from airborne LiDAR data and the measured value is 0.93, the RMSE is 1.72m, and the standard nRMSE is 5.84 %, according to the experimental results. With the measured value, RMSE=1.03m, and standard nRMSE= 3.49 %, the tree height retrieved by mixing point cloud and optical image is R²=0.95. The precision of individual tree height extraction from airborne LiDAR point cloud data can be greatly improved by combining optical images.

In this paper, a registration method for raster topographic maps based on logistic regression is proposed. Firstly, the registered topographic maps are transformed into a matrix, and the initial data set is constructed by finding the registration point information from the TAB file, and using the data set to train the logistic mode. Then, we introduce a sliding window and combine with the trained logistic model to register the topographic maps. Experiments show that the registration method proposed in this paper can automatically register raster topographic maps quickly and accurately, and achieve the effect of simplifying the registration process.

Considering the current method of image compensation based on RFM that needs additional affine transformation coefficients, a method of satellite image positioning based on refined RFM is proposed. Firstly, using the block adjustment result, a virtual control grid for refining RFM is generated. Secondly, to improve the accuracy and efficiency of refined RFM, ridge estimation and the LU decomposition are used. Finally, taking the Mapping Satellite-1 image as an example, the effectiveness of the refined RFM in image positioning is verified. The experimental results show that the proposed method is a high-precision transfer of block adjustment results, the refined RFM is unified in form, and convenient for image positioning.

Buried object detection methods based on deep learning require a lot of annotated data, and most of them rely on pretrained models. To solve these problems, a buried object detection method that only needs a small amount of annotated data and has a short training time is proposed. This method integrates the attention mechanism into the U-net model, obtains the pixel-to-pixel predicted grayscale, and finally extracts the region of interest for target localization. The experimental results show that this method can accurately detect buried objects with only a small amount of annotated data in the actual B-scan images of ground penetrating radar.

The segmentation of crop disease is an important task for plant image processing. Fully supervised segmentation methods are commonly used to segment disease on plant leaves, and a large amount of manually annotated information is needed. In contrast, weakly supervised segmentation methods have the advantages of reducing the workload of manual annotation. In this study, a weakly supervised segmentation method for plant disease was proposed for the segmentation of rust in apple rust images using the procedure below. A classification model was trained using images of both diseased and healthy leaves, and class activation maps (CAMs) of disease were obtained as pseudo labels for a further segmentation of the disease. The segmentation method using CAMs only achieved an Intersection over Union (IoU) score of 0.45. Then point-auxiliary labels and line-auxiliary labels of disease were also constructed to improve the accuracy of the disease segmentation. The IoU values resulting from the two schemes – the point-auxiliary labels + CAMs and line-auxiliary labels + CAMs methods reached 0.64 and 0.74, respectively. Moreover, the proposed method was also applied to the segmentation of disease on whole leaves. Furthermore, the proportion of disease in whole leaves can be also obtained, and the coefficient of the correlation between the obtained disease proportion and true disease proportion was 0.89. The results suggest the feasibility of the proposed method to segment plant disease and evaluate the severity degree of the plant disease.

Space-based remote sensing is an important way of detecting many types of land targets. For the purpose of taking cover, land targets have a strong demand for avoiding space-based remote sensing reconnaissance. In practice, space-based reconnaissance will produce huge data, which are unbearable for human-beings. Therefore, the data processing must rely on artificial intelligence technology such as deep neural network. Many previous works show that the existing intelligent target detection algorithm based on deep neural network will be affected by perturbations. Firstly, this paper establishes a target detection method based on the Faster RCNN framework, and then three types of disturbances methods are studied to help the mobile radar to counter the typical space-based artificial intelligence detection algorithm. The simulation results show that the three types of disturbances methods can fool the typical target detection technology based on deep neural network.

In order to carry out targeted drought early warning for each region of Liaoning Province, Liaoning Province is divided into five research areas: Liao Dong, Liao Nan, Liao Xi, Liao Bei and Liao Zhong. The corresponding remote sensing drought monitoring index set is constructed respectively, and the drought monitoring model of each region is constructed by combining the radial basis function neural network. The results show that the average accuracy of the remote sensing drought monitoring model constructed by FY-3D / MERSI satellite remote sensing data combined with radial basis function neural network is more than 85%. The accuracy of the model in the study area of southern Liaodong is up to 93.76%, and the root mean square error is 7.69. Through the drought monitoring model, the large-scale soil moisture inversion in Liaoning Province is carried out. The results are basically consistent with the actual soil moisture situation, and achieve a better inversion level, which can be used for future regional soil moisture remote sensing inversion to provide a new reference direction.

Synthetic aperture radar (SAR) image registration is significant for mapping, measurement, navigation, and so on. However, it is problematic for the SAR image alignment because the multiplicative speckle noise in the image and the coherent imagery mechanism of the SAR. Therefore, an advanced feature-based image registration method for the SAR image is proposed in this paper. Firstly, the speckle noise is filtered in natural SAR image based on the weighted nuclear norm minimization, which makes the amount of the false feature point reduce. Secondly, with the defined gradient for the SAR image, the improved SIFT method is employed to extract the feature point and generate the descriptor. The experimental results show that, compared to other methods, the proposed method improves both the accuracy of alignment and utilization of feature point significantly.

With the rapid development of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a large number of coastal infrastructures such as subsea tunnels and cross-sea bridges have been constructed. Meanwhile, construction processes such as land reclamation, piling, and sinking pipe installation have a certain impact on the water environment. Traditional sampling methods have been applied in the water condition monitoring, however, Remote Sensing (RS) and Geographic Information System (GIS) based technology have advantages in methods, efficiency, and scale. Thus, in this study, the construction processes of both Shenzhen Bay Bridge (SZB) and Hong Kong-Zhuhai-Macao Bridge (HZMB) are surveyed by using spatial analysis, visualization, and other methods. Moreover, suspended sediment concentration (SSC) and chlorophyll-a (Chla) concentration of the surrounding water before, during, and after the construction have been monitored. A comparative analysis has been adopted to illustrate the impact of the bridge construction process on the water environment. This study can provide an essential basis by using RS and GIS technology for studying the impact of largescale coastal infrastructures on the surrounding water.

Interrupted sampling repeater jamming (ISRJ) is a new type of coherent jamming for wideband radar systems. By copying and repeatedly forwarding the radar transmitting signal slice by slice, ISRJ can generate a series of false targets in the range direction, which significantly impairs radar’s ability to detect and track targets of interest. Therefore, an advanced time-frequency (TF) filtering method for ISRJ suppression is proposed in this paper. Firstly, the radar received echo signal is transformed into TF domain through short-time Fourier transform (STFT). Secondly, based on the discontinuity and high intensity of ISRJ, the ISRJ contaminated regions can be mapped precisely in the TF image by means of histogram energy analysis and subsequent TF energy accumulation. Finally, these regions are removed by a constructed adaptive filter and the jamming-free pulse compression (PC) results can be obtained. Simulation results reveal that, compared with other competing filtering methods, the proposed method can effectively suppress ISRJ and show better robustness under different circumstances.

This paper used the method of extracting abandoned land after classification of land use types to study the abandoned land situation in Zhaotong City from 2013 to 2019. Firstly, random forest classifier was used to classify the land use types of Zhaotong City from 2013 to 2021 through the collected sample point data of various land use types and Landsat8 remote sensing image data, and good accuracy was achieved. Then, according to the definition of abandoned land in this study (farmland that has stopped farming for more than one year is considered as abandoned land), the abandoned land map of Zhaotong City from 2013 to 2019 was obtained by comparing the land use type maps of the previous years. Finally, the abandonment situation of Zhaotong City is discussed based on the results of field investigation.

Hot rolled steel strip is usually detected by image processing for defects, but it is often affected by light, and conventional image processing methods cannot effectively detect defects with small areas. In this paper, an image processing method is proposed to overcome the effect of fill light for the detection of steel strip surface damage by convolutional neural network multiscale detection method. In the target detection part, K-means++ clustering was performed on the anchor size, while the S-SPP module for multi-scale detection was introduced to further improve the detection of small-area damage. After training the model with the NEU-CLS dataset, the accuracy reached 94.6% and the detection speed was 27.3 ms. Through comparison experiments, the experimental results show that the proposed method of image processing can detect steel strip damage more accurately and faster, which provides a feasible method for practical application in factory scenes.

In recent years, Differential Synthetic Aperture Radar Interferometry (D-InSAR) can effectively measure the surface displacement caused by earthquake, and has been widely used. On January 8, 2022, an earthquake of Ms6.9 with a focal depth of 10km occurred in Menyuan County, Qinghai Province. In order to more accurately monitor the surface deformation caused by the earthquake, in this study, Sentinel-1A ascending and descending orbit SAR images are used and processed by four-pass D-InSAR technology to obtain the coseismic deformation field , and the deformation results are analyzed and verified. And the results of adaptive, boxcar and Goldstein filtering are analyzed qualitatively and quantitatively, the phase unwrapping results of each filtering result are compared and analyzed. The results show that the coseismic deformation field of the earthquake ruptured along the northwest southeast direction (NWW-SEE), and the surface deformation of radar line of sight (LOS) are -53.6 ~ 68.8 cm for descending orbits, -69.1 ~ 20.5 cm and -42.9 ~ 41.9 cm for ascending orbits, respectively. For the earthquake area, Goldstein filter is better than the other two filters, which can better suppress noise and maintain phase information and its continuity. The research results have important reference significance for seismic deformation inversion and filtering methods.

The use of remote sensing images for earthquake disaster information extraction and disaster judgment is one of the effective ways for post-earthquake disaster recognition and assessment. In this study, the remote sensing images of Longquan Village in Longtoushan Town before and after the 2014 Ludian 6.5 magnitude earthquake in Yunnan Province were used to extract building information using the maximum likelihood estimation (MLE) in supervised classification. Combining with the vector data of building distribution plotting in Yunnan Province, we explored the rapid recognition of the damage degree in the severely-stricken areas by using the spatial change detection analysis. The research results are as follows. (1) For small areas like the earthquake severely-stricken areas, the MLE can extract the building area very simply and quickly, with a good recognition effect. (2) When applying the spatial change detection method, the severely-stricken areas can be divided into severe damage and light/moderate damage zones according to whether the area change exceeds 50%, and good recognition results can be obtained, with a correct rate above 60%.

With the rapid development of internet technology and geographic information technology, the research and application of GIS based on browser/server has been extended to many industries and fields, such as emergency, military, aviation, geological exploration and so on. Collaborative plotting is based on the map service published by GIS data and performs collaborative work on two-dimensional maps, but the traditional plotting systems are single-user plotting, and each user works independently, which cannot meet the requirements of multi-user collaborative plotting. Based on WebSocket data push, combined with WebGIS map service and other technologies, this paper presents a design scheme of emergency situation map collaborative plotting system. The purpose of this paper is to study and support multi-user operations such as real-time asynchronous map plotting and map editing, put forward an exclusive control mode to realize multi-user cooperative work and complete the exchange and perception of current environmental plotting information, and achieve the purpose of seamless exchange, coordination and synchronization of command decision-making, planning and deployment, so as to predict the trend of development and provide decision-making services for emergency command applications. The test results show that the method and the designed system can greatly improve the work efficiency in multi-user emergency plotting and win time for emergency command.

Due to the increase in data quantity, ship detection in Synthetic Aperture Radar (SAR) images has attracted numerous studies. As most ship targets are small and cover a few pixels in SAR images, the commonly used intersection-over-union (IoU) metric which is sensitive to the location deviation of the bounding box is not suitable to measure the distance between two small ship boxes. To solve this problem, this paper proposes a small ships-oriented detection method based on YOLOX. First, as an anchor-free one-stage detector, YOLOX can achieve state-of-the-art performance without extra anchor parameters. To make a balance between detection accuracy and speed, YOLOX-tiny is adopted as the baseline network. Then, a modified Gaussian Wasserstein distance is proposed. By modeling the bounding boxes as 2D Gaussian distributions, the Modified Wasserstein Distance (MWD) can be used to measure the similarity between the boxes in network training and post-processing. Finally, the proposed method is verified on Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0), and the experimental results show that the proposed MWD can effectively improve the detection performance on small ships.

Remote sensing satellite mission planning is one of the hot issues in the space engineering research field, and a large number of mission planning approaches have been proposed in related research work. Numerous mission planning schemes were constructed for different mission requirements. How to evaluate the merits of the schemes is of great significance to improve the quality and effectiveness of remote sensing satellite missions. Based on the analysis of the basic problems of remote sensing satellite mission planning, a technology framework of mission scheme evaluation is proposed, and an evaluation index system for remote sensing mission planning schemes is constructed, including mission completion rate, planning timeliness and resources occupancy. A TOPSIS-based evaluation model is proposed to calculate the valuation of mission scheme according to the index system. The case study shows that the mission planning scheme evaluation approach proposed in this paper is feasible and effective.

Rotated target classification is a key problem in the automatic target recognition of sonar images. We propose herein a novel rotated target classification method of sonar images featured by classification after alignment. The proposed method first aligns the orientation of the rotated targets to normal orientation, and then uses the aligned images to train and test Convolution Neural Networks (CNN) to predict target categories. On the basis of rotated target detection of Synthetic Aperture Sonar (SAS) images, the proposed method was applied to classify the detected rotated targets and compared with the widely used data augmentation method. The results demonstrate that the proposed method significantly improves the classification accuracy, accelerates both the training and inference of CNN, and decreases the number of parameters of CNN.

Object detection in remote sensing images is a challenging task in field of computer vision since detection performance is negatively influenced by complicated background and various object size. However, most studies have only focused on object appearance, with only a few taking into account scene information, which is closely related to existence and category of objects. In this paper, we put forward a new method by integrating scene information into detection with aim of generating more powerful feature. Specifically, we made use of GRU cell, a special kind of RNN, in order to enhance object feature. The proposed method was verified through experiments on a challenging dataset, i.e., DOTA. Compared to the baseline model RoI-Transformer, the proposed method has achieved around 2.7% improvement in terms of mAP, which is initial attempt to integrate scene information into object detection.

The Yellow River Delta wetland is the largest estuarine delta wetland in China. Identifying and classifying the Yellow River Delta wetlands by remote sensing is of great importance to promoting the monitoring and sustainable development of the Yellow River Delta wetland. In this paper, an ensemble algorithm based on an adaptive weight fusion strategy was proposed to identify wetland types in the Yellow River Delta based on Sentinel-2 data, DEM data, and the constructed vegetation and water index. The experimental results show that the classification performance of the ensemble algorithm using the weight fusion strategy outperforms that of a single machine learning classifier, with an average improvement of 9.88% in overall accuracy (OA) and 11.04% in Kappa accuracy. In addition, we select features that can reflect wetlands such as raw band information, spectral index, slope, and elevation, and found that raw band information and spectral index are the most important classification variables by ranking the importance of features.

To provide safe and secure road space is a very important consideration for road administrators, and road evenness has a great impact on driving safety and riding comfort for road users. Furthermore, frequent and quantitative monitoring of the road evenness is a critical issue to establish PMS (Pavement Management System). In order to promote the PMS in local area, we investigate the evenness in local cities, using a new type of compact profiler and present evenness conditions combining different pieces of road information. In order to conduct the study, we used the Digital Road Map (DRM) and ESRI ArcGIS. Because, the DRM electronic road map has been built for road management and vehicle navigation systems for the purpose of linking road network and geographical information. Different road roughness conditions are shown in IRI, each road category and land use characteristics.

Aerosols are important pollutants affecting the quality of atmospheric environment. With the development of space remote sensing technology, using satellite remote sensing to retrieve aerosol has become an important method. In order to solve the ill-posed problem of aerosol retrieval and to mine the aerosol information of satellite signals to a greater extent, this paper proposes a method based on deep confidence network to realize the aerosol retrieval of MODIS sensor. AERONET station data and satellite data in the Beijing-Tianjin-Hebei region of China were selected, and the sample dataset was constructed according to the reasonable spatio-temporal matching principle. By setting relevant parameters, the model is trained and tested, and the optimal network model is found. The aerosol measured data from independent AERONET sites were selected for accuracy evaluation, and the accuracy reached 97.78%. Compared with the traditional aerosol inversion algorithm, the proposed method achieves high precision and high efficiency of aerosol inversion, and improves the stability and spatio-temporal adaptability of aerosol inversion.

As the data basis of 3D geographic information service, 3D model is widely used in 3D terrain visualization, smart city, geological monitoring and other fields. 3D laser point clouds have become one of the main data sources for 3D GIS modeling due to their high precision and fast modeling. When researchers mesh laser point clouds and generate some elongated triangles due to the rule constraints of the algorithm itself, elongated triangles can cause the burden of mesh optimization, affecting the efficiency of mesh reconstruction. In this paper, based on the incremental insertion algorithm, we mitigate the adverse effects of elongated triangles by constructing a non-fully partitioned KD tree index and introducing control points to influence the insertion order of point clouds. We use the proposed algorithm, BRIO and KD tree algorithms to reconstruct Bunny and Horse. We found that the proposed algorithm is superior to the other two algorithms in time efficiency and can suppress the appearance of elongated triangles well.

The issue of environmental risks caused by carbon emissions has become an important content of attention. The multi-objective optimization problem of minimizing the incremental cost of input and maximizing the incremental benefit obtained is related to the development of carbon emissions to the environment. Aiming at this problem, this paper proposes a carbon emission optimization scheme based on an improved genetic algorithm to maximize environmental value in multi-objective problems. First, in order to better reflect the efficiency of pollution caused by carbon emissions, the improved genetic algorithm is used to obtain multiple pareto solutions under the combined optimization of different transformation schemes; Secondly, the gain obtained by unit incremental cost is used as the evaluation index; Finally, through experimental verification, it can be seen that the algorithm proposed in this study has certain advantages. At the same time, in terms of environmental risks caused by carbon emissions, it is not necessary to select all the transformation schemes. Better results can be obtained by optimizing them according to actual conditions.

The application of consumer grade hardware and complexity scenes are the key limits for high accuracy of GNSS positioning and navigation service in smartphones. In the study, the comparisons for the quality analysis of GNSS observations among Huawei Mate 20, Xiaomi MI 8 and Huawei P 20 smartphones was firstly carried out. Secondly, we developed the receiving software on the Android platform for connecting the network RTK service. The high quality GNSS observations can be achieved after removing the atmosphere errors with network RTK service. Then, the high accuracy positioning results can be carried out in smartphones. The study shows that the static positioning accuracy of Mate 20, MI 8 and P 20 can converge to the centimeter-accurate within 20 minutes and the ambiguity-fixed solution accuracy can reach the centimeter by using network RTK positioning method. The kinematic positioning accuracy was greatly affected by the observation environment. The horizontal positioning accuracy of the three smartphones is 0.31 m, 0.021 m and 1.80 m, respectively.

This paper investigates state-of-the-art deep learning techniques to achieve automatic architectural style classification of the Chinese traditional settlements. First, a new annotated dataset is built with six typical Chinese architectural styles, consisting of over 1000 web-crawled images and an original image collection of Chinese traditional settlements. Second, a state-of-the-art convolutional network named DenseNet is benchmarked on the new dataset to learn the effectiveness of the deep learning networks. Yet, the DenseNet network suffered server overfitting on the small-sized new dataset. Third, to overcome the common overfitting problem, a new deep learning framework named DenseNet-TL-Aug is developed by leveraging transfer learning (TL) and data augmentation (DA) techniques, e.g., AutoAugment. The experimental results demonstrate that the new developed framework achieves much better classification performance in classifying the Chinese traditional style images than the original DenseNet, significantly mitigating the overfitting problem. This study will contribute to automated landscape gene recognition as well as the design and development of traditional tourism.

Filtering is one of the most important processes before applications such as Digital Elevation Model (DEM) generation, building reconstruction and tree extraction. Progressive-TIN-Densification (PTD) filtering has been proved to be effective in different types of terrain; however, PTD filtering highly relies on the predefined parameters, i.e. iterative angle and iterative distance. To improve the performance and adaptivity of PTD filtering, in this paper, we present a slope-constraint PTD (SCPTD) algorithm. The main contribution of proposed algorithm is a strategy to remove landcover objects with loose preset parameters. To test our method, seven sets of point cloud from ISPRS Working Group III/3 are utilized to test the validity of the proposed method. And fifteen samples with manual classification are used to analyze the proposed method quantitatively. Experimental results suggest that our method is effective; compared to classic PTD filtering, the total error have reduced in fourteen samples.

Mangrove species classification is particularly important for coastal wetland protection and global warming mitigation. However, it is challenging to identify species-level differences in practical applications. In the paper, we propose an object-oriented multi-feature ensemble classifier for the fine classification of mangrove species. First, mangrove images are segmented into objects by the multi-resolution segmentation method. Second, multiple features of each object are extracted by feature calculation methods (Gray Level Co-occurrence Matrix, Standard Deviation, Mean, etc.), and appropriate features are selected for species classification by weight estimation. Finally, the selected features are fed to an ensemble classifier to generate the final mangrove species classification results. Experiments performed on in-situ unmanned aerial vehicle (UAV) images collected in Yingzai, Guangdong Province demonstrate that the proposed multi-feature ensemble classifier achieves superior classification results to its single classifier counterparts.

UAV (Unmanned Aerial Vehicle) trajectory planning in emergency response mapping has the characteristics of complex spatial and temporal environmental constraints and diverse trajectory planning schemes, and the existing trajectory planning is mainly based on empirical judgment with low program reliability, lacking comprehensive consideration of the occurrence of complex and variable special situations in the emergency process, resulting in low accuracy and reliability of mapping results. In order to realize the rapid response of post-disaster emergency response mapping, multi-UAV collaborative mapping scheme and optimization objectives are constructed, taking into account the constraints of emergency response mapping task demand, priority, geographic environment of task area and UAV mapping resource capacity, etc. The initial perception of the environment is completed through the pre-trajectory planning of multi-UAV, and multi-UAV collaborative trajectory planning based on PSO (particle swarm optimization) is proposed based on the perception information, so as to the problem of repeated data collection and collision between UAV in the process of multi-UAV flight is effectively avoided.

Urban expansion is a complex spatiotemporal conversion process. Cellular automaton (CA) is a grid dynamic with features including spatiotemporal discrete and state concise, using simple local rules to simulate the spatiotemporal evolution process of the complex systems. Based on the principle of cellular automata, the Artificial Neural Network Cellular Automata (ANN-CA) model of Geographic Simulation Optimization System (GeoSOS) platform is applied to simulate the land use status of Nanning during 2000-2010 and 2010-2020. The simulation results indicate that the expansion direction of Nanning City in 2030 is along the Yongjiang River, which is more in line with the future land planning direction of Nanning City. The simulation results can provide valuable reference information and useful data in making macro decisions and in planning land-use by government agencies.

Aiming at the problem of the single data source in PM2.5 prediction, a PM2.5 DNN-LSTM hybrid neural network prediction model that takes into account climate factors is proposed. First, the DNNnetwork is used to abstract the characteristics of climate and seasonal factors and climate factors as an additional part of the prediction process. Input and analyze in collaboration with LSTM network. Experiments with pollution data and weather data (sampling interval of one hour) are collected from monitoring sites in Beijing from 2010 to 2014, and comparing the DNN-LSTM model with other prediction models, the results show that this model is compared to LSTM. The RMSE of the model is reduced by 10.71%, which is 5.52% lower than the RMSE of the multi-source data fusion LSTM model. Research shows that the multi-source data fusion DNN-LSTM model proposed in this paper has better predictive ability. Compared with the LSTM model, the RMSE of this model is reduced by 10.71%, compared with the multi-source data fusion LSTM model, the RMSE is reduced by 5.52%, compared with the LSTM model, the MAE is reduced by 21.55%, and compared with the multi-source data fusion LSTM model, the RMSE is reduced by 12.94%.

The Moderate Resolution Imaging Spectroradiometer (MODIS) has 36 channels covering the visible to far infrared band range with a resolution of 250 m to 1 km, and is important for detecting fires in large areas. Traditional fire detection algorithms mainly rely on thermal infrared channels using threshold or contextual methods. Such methods usually fail to detect small fire and often misidentify high-temperature objects on the surface. This paper proposes a new adaptive fire detection algorithm, which focuses on two methods to improve the accuracy of fire detection. First, single-channel and multi-channel test conditions were added and new contextual algorithms were adopted; second, a method for weighting the fire test conditions based on the test conditions for differences in the sensitivity to fire was proposed. This method reduces the issue of small fires being overlooked because they do not satisfy certain test conditions. In addition, a priori database was built using twelve-year US wildfire reference records and highest confidence fire data in MODIS fire products, adaptive thresholds suitable for fires were selected using the bubble sorting method based on the radiation characteristics of global fires. Testing results show that the improved algorithm improved the accuracy of small fire identification and reduced the false detection rate of pseudo-fires.

As the basis of intelligent vehicle environment identification, street sign recognition is a necessary condition to assist safe driving. It is of great significance for realizing automatic driving, improving intelligent transportation system and promoting smart city construction. Traditional traffic sign recognition technology is limited for complex scenes, and the recognition effect is also affected by weather and light. The road sign recognition system based on deep learning includes street sign detection and street sign recognition. YOLOv3 deep learning object detection method is adopted for road sign detection, and CCTSDB standard data set is used as experimental data to perform operations such as region trimming, batch conversion of data format, BM3D noise reduction and image preprocessing and enhancement technology. 1800 images were selected to form the training set and test set, which ensured the high efficiency and timeliness of the road sign detection algorithm. The road sign recognition system adopts the image recognition method of Darknet-53, and the algorithm is optimized. The trained system is detected under the test of CCTSDB dataset, and the detection accuracy is over 97%, and the detection time reaches 0.021s. Therefore, the system meets the requirement of accurate real-time processing of traffic sign detection and recognition system.

With the rapid change of the urban landscape, the construction of the underground cable network, as the "lifeline" of the city, is also being continuously promoted, twenty-four hours a day, to ensure the operation and development of the city. However, due to the complexity of urban underground cable laying, the previous management methods based on two-dimensional data are not only inefficient and complex, but also need to be improved in accuracy. In order to solve this problem, this paper adopts the design concept of "two-dimensional integration," aims at intelligent and information-based urban cable system, applies geographic information technology to urban underground pipelines, and studies the key technologies of urban underground cable and pipeline intelligent management system. The system realizes the transformation of urban underground pipeline from original point cloud to three-dimensional model, and establishes the spatial relationship between various elements of the underground pipeline, which facilitates the information management of urban underground pipeline. It provides an intuitive and intelligent management platform for the management of urban underground cable network. It is the application of geographic information technology in urban underground pipelines and has certain value.

Aiming at the issues of low tracking accuracy and model drift in complex scenes, such as occlusion, for satellite video vehicle tracking, an improved SiamRPN++ tracking method for satellite video is proposed to achieve high-precision sustained vehicle tracking. Firstly, considering the tiny size of satellite video vehicles, the bounding box size constraint of SiamRPN++ is reduced to 5 × 5 pixels, so as to obtain a more accurate target bounding box, which can reduce similar targets and background interference, and improve tracking accuracy. Then, the maximum value (F_max) and average peak-to-correlation energy (APCE) of the classification score map are calculated to monitor the target state. Finally, when the target is monitored to be occluded, the inertial mechanism is employed to predict and correct the position of the occluded target to achieve continuous tracking. Experiment results based on the expanded XDU-BDSTU dataset show that the precision rate and success rate of the proposed method may reach 90.22% and 55.26%, respectively, which are 15.11% and 15.52% higher than those of the original SiamRPN++, respectively. The proposed method may enable continuous, high-precision tracking of satellite video vehicles at 52 FPS, with excellent real-time performance.

There are high risks in the exploitation of shale gas by fracturing, which may cause a series of environmental problems, such as pollution of groundwater, surface water and air, safety of water resources, occupation of land resources, obstruction of biodiversity, noise pollution and traffic problems. In order to understand the research progress and its impact on the environment, this paper takes 5845 shale fracturing research papers included in the core of SCI of science network as the data source, analyzes and interprets the current shale research literature and determines the research hotspot and development direction through Citespace, Biblishiny and other software, taking keywords and citation relations as the research objects. The overall situation of shale fracturing research is shown, which provides reference for researchers' scientific research topics. In the evaluation of reservoir fracturing, the diagenesis, pore structure, brittleness index and other factors of shale source rock reservoir are considered to predict and analyze the reservoir fractures, so as to optimize the shale fracturing technology and achieve effective productivity improvement. After the completion of hydraulic fracturing construction, the amount of liquid flowing back to the ground is increasing, which makes the metal ion content in the whole ground further improve, causing serious environmental quality problem. Shale fracturing flowback wastewater treatment has become the key content of the development of the whole industry. Relevant staff need to conduct in-depth analysis on the water quality of wastewater, and select appropriate treatment methods to avoid secondary pollution.

In recent years, deep-learning-based hyperspectral image (HSI) processing and analysis have made significant progress. However, models with high performance require sufficient training samples because scarce labeled samples limit their generalization ability. To solve this problem, we adopt a self-supervised learning strategy and conduct self-training for a neural network model by obtaining different views of the same sample (positive pairs). As a result, the network can learn representative features for classification from unlabeled samples. In addition, to increase the spatial receptive field compared with the use of conventional convolutions, we use the transformer to capture long-distance dependencies for feature enhancement and adequately combine their advantages. Experimental results on two publicly available HSI datasets demonstrate that the proposed method can extract robust features through self-training on unlabeled samples and can be adapted to HSI classification tasks under the small sample conditions.

As ecological priority has become a central theme of current development, the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone is contributing as a guiding model to China’s ecological and green integrated development. From a perspective of ecosystem service functions, in particular, geographical locations and land habitats for animals and plants, we provide an analysis of the aforementioned Demonstration Zone regarding the distribution, connectivity, and accessibility of its ecological sources, in order to select strategic zones and optimize ecological spaces, from remote sensing imagery. Our analysis was based on the evolution of the ecological base and the Sources-Resistance Surface-Corridors framework integrating local conditions and characteristics. Through navigating remote sensing images of the target area, results indicated that a considerable change occurred in the plant community in the Demonstration Zone over the recent three decades, caused by the severely damaged wetland ecosystem and the limited communications among species due to urban expansion. In addition, the study revealed poor connectivity among the ecological and geographical sources. Accordingly, we propose a set of measures to avoid landscape fragmentation, prioritize ecological protection, and improve the quality of habitats in the geographic location.

Mineral prospectivity mapping (MPM) has been an essential part of mineral exploration; various algorithms have been introduced for detecting mineralization related anomalies from multi-geoinformation including geology, geochemistry, geophysics and remote sensing dataset. With much attention paid to technical development of methods used in MPM, this study proposed new insights into the mineral prospectivity mapping based on our previous studies regarding the applications of different machine learning algorithms for prospects demarcation of the Hezuo-Meiwu District, West Qinling Orogen, China. With applied algorithms, such as maximum entropy model (MaxEnt), random forest (RF), deep auto-encoder network (DAE), convolutional auto-encoder network (CAE), convolutional neural network (CNN) etc., the thesis of this paper highlights the importance of datasets collected and proposed a shift to research on interpretable learning.

In view of the current situation that most of the current isobaths automatic simplification algorithms are aimed at a single depth contour and it is difficult to unify the automatic simplification and merging process, we propose an automatic simplification and merging algorithm of isobaths based on surface simplification. Firstly, the basic principles of surface simplification and rolling ball synthesis of seafloor topography are analyzed, and then high-precision digital depth model (DDM) is constructed using nautical chart data as the original three-dimensional data; Then, on this basis, the high-precision DDM is simplified by using the rolling ball transform; Finally, based on the simplified DDM, the isobaths are extracted reversely to realize the purpose of automatic simplification and merging. The experimental results show that: in typical regions, the proposed method can automatically identify and process the regions to be simplified and merged, and our method also improves the automatic synthesis quality of isobaths.

The water quality parameters (WQPs) at the constructed wetland of the lakeside zone are fluctuating. In order to accurately estimate the water purification efficiency of the lakeside belt, the remote sensing technology by satellite has a great advantage to complete such a task. In this study, back-propagation neural network and polynomial regression are compared in remote sensing estimate Total Nitrogen (TN), Total Phosphorus (TP), Nitrate Nitrogen (NN), and Ammonia Nitrogen (AN) concentrations in constructed wetland water quality. The result shows that the BP neural network algorithms outperformed the polynomial regression algorithms in the estimate AN and TN. However, the polynomial regression algorithms have achieved better performance in the estimate NN and TP. Moreover, the best algorithms produce about 60% of rRMSE in all WQPs in this study. As to mean normalized bias (MNB) result, the overall estimate by the BP neural network algorithms is lower than the measured data. In addition to TP, the empirical model is the opposite. This study could provide some reference for the remote sensing estimate of the water purification efficiency in constructed wetlands. Furthermore, BP neural network performance is more stable than the polynomial regression.

UAV remote sensing technology is a new application technology optimized by various advanced means. In this study, the UAV remote sensing technology is used to build 3D models of ancient buildings and evaluate the accuracy of 3D models. The research proves that the UAV remote sensing technology has the advantages of low cost, high efficiency, high flexibility and so on compared with the traditional 3D modeling method in the aspect of ancient building digitization. It is widely used in 3D model construction and panorama production. The model obtained has high precision, which can provide intuitive data for designers and researchers. The UAV remote sensing technology can accurately provide data for the protection and maintenance of ancient buildings. This paper takes Zhoushan ancient architectural heritage as the research object, and plans to use UAV aerial survey technology to take pictures of ancient buildings from multiple angles, generate panoramic images, establish three-dimensional models, and analyze model accuracy. This paper is of great significance to the application of consumer UAV aerial survey technology in the digitalization of ancient buildings and the protection of cultural relics.

In view of the demand for dynamic displacement safety monitoring of super-high buildings, bridges, dams and other structures, and the problems of difficult high-frequency monitoring with GNSS technology and weak algorithm robustness, this paper introduces an accelerometer to design a simple multi-rate Kalman filter algorithm, and constructs a GNSS/accelerometer robust monitoring model for dynamic displacement of structures based on the robustness principle. This model not only improves the monitoring density and accuracy, but also improves the robustness of the algorithm. In the ground shaking table simulation experiment, the root mean square errors of dynamic displacement monitoring at three frequencies of 0.5 HZ, 1 HZ, and 1.5 HZ are 5.3mm, 6.8mm, and 7.4mm, respectively. Compared with pure GNSS technology, the monitoring accuracy is improved by 24.2%, 29.1%, and 28.8%, respectively. The monitoring results have no obvious gross errors, and the model is more effective for environments with relatively lower frequencies. Therefore, the model proposed in this paper effectively solves the problem of high-frequency dynamic displacement monitoring of structures, and improves the monitoring accuracy and robustness.

With the increasing impact of human activities on the natural ecological environment, it is of great significance to realize the dynamic monitoring of ecological quality status and obtain effective change information for sustainable development and regional space ecological environment protection. Based on the remote sensing cloud computing platform of Google Earth Engine (GEE), with the help of remote sensing based ecological index (RSEI) and taking the Landsat Image of a demonstration area in the Yangtze River Delta on ecologically friendly development from 2015 to 2021 as the data source, this paper monitored and evaluated the regional ecological environment quality. The results show that the average value of RSEI in the region decreased from 0.626 to 0.543 from 2015 to 2021, showing a downward trend. Spatially, the areas with good ecological grade are mainly concentrated in Qingpu District of Shanghai, the areas with poor and fair ecological grade are mainly distributed in Wujiang district and the south of Jiashan County in Suzhou City, and the areas with basically unchanged ecological grade mainly include the south of Qingpu District. The GEE platform benefits from its advantages of quick and easy data processing and better availability of remote sensing image data during the research period, which provides a reliable reference for the ecological environment quality assessment of a demonstration area in the Yangtze River Delta on ecologically friendly development.

The chi-square deception detection algorithm is widely applied in tight GNSS/INS integrated navigation system of deception detection, and there are many factors that can influence the effect of chi-square deception detection algorithm. Among them, INS accuracy directly affects INS positioning error and indirectly affects the state transition matrix and observation matrix of Kalman filter, so as to have an impact on deception detection algorithm. Therefore, the analysis of the impact of INS accuracy on deception detection algorithm helps user adjust appropriate parameters of deception detection algorithm based on INS precision. The simulation results show that the INS accuracy has a significant impact on the deception detection algorithm when the state propagation interval is long. Contrarily, the effect is not obvious in the short interval of state propagation. Thus, the effect of INS accuracy can be reduced by adjusting the extrapolation period of deception detection.

Aiming at the problems of incomplete audit mechanism and low degree of automation and intelligence in data sharing service of surveying and mapping results, this paper puts forward a method of compiling and sharing metadata of surveying and mapping results based on intelligent contract technology. Firstly, the metadata of surveying and mapping results is compiled, and the metadata system of surveying and mapping results oriented to smart contract is constructed. Then, according to different conditions of open data sharing, the intelligent sharing method of surveying and mapping results data based on metadata is designed. Finally, the metadata function of smart contract is expanded, and the right of sharing data based on metadata is realized. This method improves the automation and intelligence of resource sharing and exchange of surveying and mapping results catalogue, and lays a technical foundation for the realization of sharing and exchange system based on blockchain.

In this study, the characteristics of the air quality in current years were comprehensively analyzed according to the data between 2016 and 2020. The difference of six pollutants' concentrations within five years in Beijing, Tianjin, and Hebei were compared at the same time. The control effectiveness in three areas was evaluated. Control measures would benefit the air quality improvement, which has the potential to give advice for the area that are less effective. Furthermore, comparing the change of concentration of six major pollutants within 5 years, the effectiveness of control measures on each concentration is evaluated. Under this circumstance, we considered different factors that contribute to the result, and found their implication on future control. From this study, it was concluded that (1) The control measure between 2016 and 2020 in Beijing-Tianjin-Hebei was effective for CO₂, CO, SO₂, PM_2.5, and PM₁₀. (2) The control of ozone and nitrogen dioxide is less effective, which requires efforts to reduce vehicle emissions and VOC emissions. (3) The effectiveness among the three places is equally effective, which indicated the positive tendency should be sustained.