This PDF file contains the front matter associated with SPIE Proceedings Volume 12797, including the Title Page, Copyright information, Table of Contents, and Conference Committee information

The purpose of this paper is to study the calculation method of engineering earth volume under complex terrain conditions of the Yellow River dam bank, using traditional section method and square grid method, as well as introducing the DEM method to measure and analyze the volume of the Yellow River dam bank. Through a comparison and analysis of experimental data, it is concluded that the DEM method has the advantages of convenient calculation, strong applicability, and accurate results. Firstly, the geographical situation and complex terrain characteristics of the Yellow River dam bank are introduced. Then, the traditional section method and square grid method for measuring earth volume are described in detail, along with the basic principles and application methods of the DEM method. The experimental data and analysis results demonstrate that the DEM method has high measurement accuracy and precision, and can effectively solve the problems associated with the traditional methods when dealing with complex terrain conditions. In summary, the research results of this paper have certain theoretical significance and practical application value in improving the calculation method of earth volume for the Yellow River dam bank project and enhancing the accuracy and reliability of earth volume calculation. The findings provide important support for improving the efficiency and accuracy of Yellow River management and administration, including scheduling, flood control, and disaster warning.

Constructing a land use transfer matrix and a TVX space, this paper analyzes the spatiotemporal changes of land use in Shandong Province from 2000 to 2020, and analyses the influence of land use changes on land surface temperature, based on the analysis results. In Shandong Province, from 2000 to 2020, urban construction land has sprawled significantly, while rural construction land has grown slowly but shows significant spatial transfer. Meanwhile, there are many mutual conversions between urban construction land and rural construction land, causing important influences on land surface temperature. The analysis shows that the sprawl of urban and rural construction land, which is a prominent feature of land use changes in Shandong Province, is closely related to changes in land surface temperature, exhibiting significant urbanrural differences.

Water is a fundamental material foundation for human survival and social development, and lakes represent a crucial freshwater resource that plays an essential role in human production, life maintenance, ecological security, regional socioeconomic development, and biodiversity conservation. Recently, remote sensing and geographic information technology have enabled large-scale, real-time, dynamic lake change monitoring, overcoming previous limitations associated with traditional field inquiry methods. However, despite the growing number of studies that utilize remote sensing and geographic information technology for lake research, there is a relative lack of independent evaluations of their development and implementation. To address this gap, this study analyzed 784 pieces of literature related to lake remote sensing and geographic information technology published in the Web of Science (WoS)TM core collection from 2003 to 2022. The analysis covered various aspects, including annual literature trends, research fields, research institutions, influential journals, core authors, highly cited literature, time trends of keywords, and research topic maps. The results indicate an upward trend in the number of publications in this interdisciplinary field, with a growth rate of 11.81% annually. China and the United States were the most active countries in this field. The interdisciplinary nature of the field is evident, encompassing remote sensing, water resources, environmental and ecological science, computer science, geomorphology, earth science, among others. Climate change, land use, wetland, and water quality change detection were identified as research hotspots. The findings of this study offer a valuable reference for relevant researchers and scholars to comprehend the evolution of the field and identify new research paths.

Based on ArcGIS Engine as the development platform and C# language, the system is designed and built to visualize the geological environment The system is designed and built to visualize the geological environment quality of Jinan city, which realizes the functions of urban geological data management, borehole drawing, evaluation and analysis, 3D visualization. It provides technical basis and strong geological data support for urban underground space planning and construction, water supply for spring It provides technical basis and strong geological data support for urban underground space planning and construction, water supply for spring preservation.

An ensemble forecast of historical climate simulations consisting of CMIP6 models was used to project years during World War II for which temperature data were missing for Hong Kong. In multi-model ensemble prediction, since the deep learning in this paper considers that GCM has dependencies between continuous values, the overall effect of prophecy may be better than machine learning. The daily temperature recorded by the Hong Kong Observatory and the extreme temperature index are used to evaluate the daily temperature forecast effect of CMIP6 in the historical period. In the 139-year record span of the Hong Kong Observatory, comparing the forecast results with and without CMIP6, the results show that except for the TN10p and TX10p cold indicators, all other indicators are on the rise, indicating that the temperature is rising.

The tropical cyclones (TCs)-ocean interaction is an important process that modulates the intensity and track of TCs, and the ocean response is modulated by pre-existing mesoscale eddies. In this study, the response characteristics of the sea surface temperature (SST) and chlorophyll a (Chl-a) concentration caused by several typhoons were analyzed, as well as the impact of the TCs’ moving speed and turning by using satellite remote sensing data and WOA18 datasets. In terms of the temperature and salinity response of the upper ocean, Typhoon Nakri induced a very strong SST cooling (6 ℃), caused two main sea areas of SST cooling on the sea surface and a long-term Chl-a bloom concentration of bloom center of 2.4 mg m⁻³. We reckoned when Typhoon Nakri interacted with the cold eddy, the nutrient-rich cold waters will flow up to the mixed layer easily and induce the growth of Chl-a concentration. The results of this case study provided reference for relevant study during and after the typhoon in the South China Sea.

This paper takes Damusi Township as a case study to explore the model for assessing the risk of geological disasters, aiming to objectively understand the level of geological disaster risk in the monitoring area and enhance the capability of geological disaster prevention and control. The paper discusses factors contributing to the occurrence of geological disasters, including slope, slope direction, curvature, roads, faults, engineering geological formations, gully density, elevation difference, distance to water system, and normalized vegetation index, etc. Two evaluation methods, qualitative and quantitative, are introduced. The qualitative analysis relies primarily on expert rating analysis, while quantitative analysis tends to rely on mathematical methods for quantification. Emphasis is placed on the combined evaluation model of Analytic Hierarchy Process (AHP) - Information Amount Method for assessing the susceptibility of disasters. According to the calculation model's evaluation results, the susceptibility evaluation results are divided into four levels according to the natural breakpoint method: none (0.000-0.242), low (0.243-0.485), medium (0.486-0.677), and high (0.678-1.000), and a regional landslide susceptibility evaluation map is obtained. In the conclusion section, the disaster initiation mechanism and motion characteristics are analysed in detail, and the degree of impact caused by the occurrence of disasters under different working conditions is analysed. Finally, the paper provides a specific disaster impact analysis, offering important reference data for disaster prevention and control.

Understanding the spatiotemporal dynamics of PM2.5 emissions from industrial heat sources is crucial for industrial reform and air pollution control. This study utilizes PM2.5 concentration data from 2012 to 2021 in the Beijing-Tianjin-Hebei region (BTH), employing spatial statistical analysis and correlation analysis. Evaluation indices, including total PM2.5 load and average concentration, were used to dynamically assess the ten-year spatiotemporal variation of PM2.5 emissions at the surface of industrial heat sources. Further analyses by category and pollution level were conducted. Results indicate that: (1) the central and southern in BTH have higher total PM2.5 loads from industrial heat sources, with the south having a higher average PM2.5 concentration. (2) Steel industries contribute the highest total PM2.5 load, while oil and gas development industries have the highest average PM2.5 concentration. (3) PM2.5 concentrations at the surface of industrial heat sources are relatively even, mainly clustering within the 60-80μg/m³ range. (4) Over the ten years, the PM2.5 concentration at the surface of industrial heat sources exhibits a fluctuating declining trend with a pronounced seasonal distribution: concentrations are highest in winter and lowest in summer, the highest concentration occurred in January 2014, reaching 124.76μg/m³.

This study examines the impact of war conflict and political instability on industrial heat sources in Ukraine, considering the contrasting backdrop of abundant mineral resources and lagging economic development. Utilizing long time series remote sensing satellite data, the research enhances statistical efficiency compared to traditional labor-intensive methods of data collection. An innovative approach combining spatio-temporal density partitioning and machine learning is employed to identify energy-consuming industrial heat sources. By analyzing the spatio-temporal distribution of these sources in Ukraine from 2012 to 2022, the study reveals a significant decline in the number of industrial heat sources during the post-war period in the region, highlighting the significant impact of the war on its heavy industry.

The identification of deformation hazards and the low efficiency of routine inspections in trunk highways present significant challenges. This paper addresses these challenges by utilizing MT-InSAR (Multi-Temporal Interferometric Synthetic Aperture Radar) technology to obtain surface deformation results from January 2021 to March 2023 in Jiaozuo City. Through the integration of highway road network vectors, a comprehensive evaluation of the occurrence of uneven settlement sections along the corridors is conducted. The findings reveal varying degrees of settlement experienced by Jiaozuo City during the specified period, with more significant surface subsidence observed in the northeast direction, attributed to the influence of mining areas. Notably, certain sections of the highways within a 1 km corridor exhibit a large annual average deformation rate and cumulative subsidence, indicating a severe impact of surface deformation disasters. This study provides valuable scientific support for the rapid and accurate assessment of surface deformation disasters in highway corridors using MT-InSAR technology, enabling a comprehensive understanding of their impact on highway infrastructure.

With the improvement of living standards, health issues are particularly concerned by people. Scarlet fever is an infectious disease with high incidence among children. The research direction of scarlet fever has gradually shifted from clinical research to spatial-temporal analysis of geography. In this study, the number of scarlet fever cases from 2013 to 2018 was obtained from China Public Health Science Data Center, combined with social factor data as relevant variables, and analyzed using spatial autocorrelation, Bayesian hierarchical spatio-temporal models, and Geodetector. Research shows that scarlet fever presents regional characteristics in China with the Qinling Mountains-Huaihe River Line as the boundary, and the cities north of the line have a higher risk of disease than cities south of the line. At the same time, traffic factors are the most important non-weather factors affecting the risk of scarlet fever. Therefore, the cities north of the boundary need to pay more attention to the risk of scarlet fever and take preventive measures when traveling to reduce the risk of infection.

For a long time, natural disasters have caused trauma to all aspects of our society, and historical disaster information records are incomplete. The coupling relationship between disaster damage and actual loss lacks spatial information. These factors make it necessary to carry out a natural disaster risk survey. Under the background of comprehensive risk survey of natural disasters, based on the NPP VIIRS remote sensing data, OSM data, "Tianditu" data to recognition of county road in Changqing District, intended for preliminary natural disaster assessment. Then finally the convergence results of two layers of 669 records of municipal roads and 121 records of municipal bridges were obtained. Based on the preliminary work of the municipal infrastructure base map, using the indicator system method to assess the risk of natural disasters on county roads. Based on the Five basic evaluation indicators in the Technical Guidelines for Highway Natural Disaster Census, calculate the County Road Risk Index (CRI) to obtain the disaster risk classification of county roads in the study district. Preliminary research suggests that there are currently no natural disaster risk points in this area, and further risk analysis will be conducted in the future.

In recent years, the development of Digital Earth technology, has been from the ground digital space digital expansion. With the increase of the number of spacecraft in orbit and the increase of its lifetime, the interference and collision risk of non-cooperative targets bring great challenges to the safe and stable operation of spacecraft in space. In order to adapt to the new development demand, improve the intelligent monitoring and early warning level of spacecraft in orbit, and improve the space integrated management and control ability, the paper puts forward the construction of the spacecraft orbit monitoring and early warning system based on digital earth. The system takes the digital earth as the bottom platform, based on the space, ground and sea data information, carries on the data extraction, the fusion and the mining, the realization of the functions of number management, situation presentation, evasive handling and requirement planning of the space spacecraft will greatly enhance the level of intelligence and automation of the on-orbit Operation Management and rapid emergency detection of the spacecraft, it provides guarantee for safe and reliable operation of spacecraft in orbit.

Transmission lines in Sichuan area play an important role in electric power system. Due to frequent geological disasters mainly based on landslide in Sichuan area, transmission towers will suffer from tilted, uneven subsidence and horizontal sliding due to ground deformation, which directly endangers the safety of power towers and stable operation of line. Therefore, it is of great significance to study the influence of surface deformation on transmission tower construction and operation under complex surface conditions. In view of the complex terrain conditions in Meigu County, Liangshan Yi Autonomous Prefecture, Sichuan Province, this paper adopts the time-series InSAR deformation monitoring technology, and calculates the SBAS-InSAR deformation rate and deformation time series on the basis of data preprocessing and differential interference processing, and obtains the geocoding deformation monitoring results. According to the deformation rate of the monitoring area, the deformation time series of the sampling point and the landslide hazard

The vegetation information system is a key component of the international ecosystem assessment system. It is of great value to construct an environmental assessment system and ecological governance through obtaining plant information from airborne LiDAR data. Focusing on Xiamen University of Technology, this study collected plant point cloud data within the research area using UAV radar technology. It then used the inverse distance weighting interpolation method to construct a plant canopy height model to obtain the appropriate tree height. Meanwhile, the estimated values were compared with actual data points to explore the adaptability of different interpolation calculation methods to extract plant information in the research area.

Taking 291 national wetland parks in nine provinces of the Yellow River as the research object, the spatial distribution characteristics and influencing factors of national wetland parks in Jiangxi Province were studied by ArcGIS spatial analysis tools and SPSS correlation analysis. The results showed that the national wetland parks in nine provinces of the Yellow River generally showed an aggregation distribution, and the overall distribution trend was "less dispersion in the upstream and more aggregation in the downstream"; From the provincial scale, it is mainly concentrated in Shandong Province, Henan Province, Shaanxi Province, Inner Mongolia Autonomous Region and other four provinces. In terms of spatial density distribution, the national wetland parks in the nine provinces of the Yellow River show a multi-center cluster distribution pattern, with Shandong Province as the core and surrounding sub-center clusters such as Shaanxi, Henan and Inner Mongolia. In terms of influencing factors, the distribution of national wetland parks in the nine provinces of the Yellow River is influenced by multiple factors such as physical geography, economy and population density, among which physical geography is the main influencing factor, and economy and population density also have great influence on the spatial distribution of national wetland parks.

The author collected, organized, and analyzed typical borehole data in Nantong City and established a database for borehole lithological stratification and stratigraphic sequence. With the help of typical borehole database and under the stratigraphic sequence-structural framework, a 3D model of the Quaternary stratigraphic sequence-structural framework of Nantong City is constructed via MVS and ArcGIS. The model includes layered contour maps and typical lithostratigraphic profiles, enabling 3D geological modeling and visualization of Nantong City. The modeling results are analyzed in conjunction with the findings of shallow seismic exploration, revealing the spatial distribution of major stratigraphic interfaces and the spatial correspondence between the undulating morphology of the bottom interface and the location of target faults.

In this paper, we selected Sentinel-1 uplinked SAR images of the hilly area in the northern Qinling Mountains and obtained the time-series deformation information of the area based on SBAS-InSAR technology. the monitoring results of InSAR technology showed that the deformation of surface slopes can be used as the prerequisite data for landslide identification, and then combined with optical remote sensing image interpretation, aerial survey data of UAV and field survey to identify potential landslides. Thirty-four landslides were successfully identified and the typical landslide deformation was analyzed. Based on SBAS-InSAR technology, the maximum annual average deformation rate of -20mm/y in the line of sight direction and -30mm/y in the slope direction was observed in the hilly areas of the northern Qinling Mountains, and the potential landslides in the territory are mainly located in the slope areas of the mounded deposits, where the pore space is more developed, the slopes are exposed bedrock, and the vegetation cover and growth are poor, which provide the material basis for landslide development. -30° range and slope direction eastward and southeastward provide the advantageous topographic conditions for landslide development. The uneven distribution of rainfall in time has a serious impact on landslide deformation. The results of the study can be used as the basis for the subsequent regional geological risk evaluation and risk zone delineation, and provide scientific reference for the key monitoring of landslides close to water systems and faults.

The traditional models for inverting sea surface temperature (SST) have relatively low accuracy and are unable to predict the SST in cloud-covered areas of remote sensing data. This study focuses on the South China Sea and its surrounding areas. Based on the 2022 Himawari-8 satellite imagery data and iQuam2 measured data, a 1x1 sampling window was selected to construct the inversion dataset. The support vector regression (SVR), artificial neural network (ANN), and LightGBM algorithms in machine learning were employed for SST inversion under two scenarios: clear sky and entire region, based on the presence or absence of cloud cover. The inversion results during the testing phase indicate: (1) The LightGBM algorithm demonstrates higher inversion accuracy for the entire region compared to the clear sky scenario, indicating its ability to effectively mitigate cloud interference; (2) Under the entire region scenario, LightGBM achieves a correlation coefficient of 0.9407, mean absolute error of 0.2335°C, and mean square error of 0.1803°C, outperforming other algorithms; (3) The inversion accuracy of machine learning algorithms is significantly higher than that of Himawari8 Level 2 products. These conclusions highlight that the LightGBM algorithm can overcome the limitations of traditional methods and achieve high-precision SST inversion for the entire region.

This study utilized remote sensing data analysis to obtain remote sensing images of rice cultivation systems. ArcGIS hotspot analysis and point pattern analysis functions were employed to analyze the changing trends and spatial patterns of rice re-planting index in the study area from 1990 to 2020, in combination with statistical data analysis. The research findings indicate a decrease in both the rice cultivation area and production in the southern regions of China, except for the Guangxi Zhuang Autonomous Region where the proportion of double-season rice cultivation has also decreased. In terms of spatial distribution, the concentrated areas for single-cropped rice cultivation have expanded, while the concentrated areas for double-season rice cultivation have decreased and become more dispersed. The overall direction of rice cultivation in the study area has gradually shifted northward, indicating a weakening influence of Zhejiang and Hubei provinces. Furthermore, the geodetector was used to identify the influencing factors of the proportion of double-season rice cultivation and the driving mechanisms behind its changing trends. It was found that compared to 1990, the explanatory power of agricultural technological progress indicators has significantly increased, and per capita disposable income of farmers has emerged as the dominant explanatory factor for the proportion of double-season rice cultivation. It is recommended that government departments provide policy support in the form of regional differentiation policies, strengthening the construction of supporting agricultural facilities, and deepening reforms on the supply side of rice production. These measures aim to stabilize and increase the area and yield of double-season rice cultivation, ensuring national food security.

To address the issues of concealment, complexity, and difficulty in obtaining information and supervision during the development, utilization, and supervision of mineral resources in China, a system of "data analysis+ground investigation+personnel access+InSAR global subsidence observation+underground mine measurement" was organically combined and closed-loop monitoring was carried out. HePan coal mining rights data were combined with multitemporal and multiplatform satellite remote sensing image data from 2020 to 2022. a thorough evaluation of the range of the main mining activities in the mine over the previous two years. The outcomes show that using InSAR technology for on-site cross-border mining verification in HePan coal mines has enabled real-time dynamic monitoring of mining behavior in the mining area, offering technical support for the standardization and normalization of underground mining behavior in coal mines.

This article extracted topics on eco-geological environment factors using “GF1-BCD satellite data”. By comparing the extraction results of different data sources and the results of existing regional surveys, we evaluate the accuracy of GF1- BCD satellite data in determining the location, boundary and type of feature elements. Evaluate the ability to identify the distribution range of various factors of natural resources remote sensing survey^[1,2]. Thus, the application capability of this satellite data in natural resources remote sensing survey and monitoring is evaluated. The results show that the satellite data of Gaofen-1B, C and D can satisfy the information extraction work of 1:50,000 engineering geology, land cover and river change elements, and provide data support for the regional ecological and geological environment survey.

Rapid acquisition of earthquake-stricken areas is helpful to carry out emergency rescue work efficiently, and also has important reference significance for the production of intensity map. In this paper, with the new generation night light image product suite (NASA's Black Marble product suite) developed by NASA as the data source, the two-time difference value method and significance test are used to detect the change area of the difference image before and after the Menyuan Ms6.9 earthquake in Qinghai Province, so as to identify and extract the earthquake affected areas. The extraction results show that the earthquake affected area in Minle County is the largest, and the macro comparison according to the field investigation site and intensity map, the extraction results are in line with the actual situation.

To solve the problem that the few measured B-Scan annotated data obtained from the ground-penetrating radar are difficult to train in a large-scale model, we propose an underground target classification and detection method that requires only a small number of data sets and a short training time. This method integrates the self-attention mechanism and network layer skip connection into the SegNet model called GPRSNet, completes pixel-to-pixel classification prediction, and finally performs target localization based on the classification results. The results show that the method can still classify and label underground targets with only a small number of labeled data sets. Due to the use of real measurement data for model training, the method can be effectively used in practical applications to detect targets in ground penetrating radar images.

Based on Interferometric Synthetic Aperture Radar (InSAR) technology, scope of the Duobao Sand Hill in Poyang Lake has defined according to the change shown by erosion/accumulation of the Duobao Sand Hill from 2016 to 2021. Research suggests that the overall trend of Duobao Sand Hill is NNE-SSW, with the dominant trend of sand hill being NNE30-35°; The sand hill is high in the southwest, low in the northeast, long between north and south (about 8977m), narrow between east and west (about 6696mm)，and has an area of approximately 22.17km² . The latitude and longitude span is 29°21′46.84″-29°26′39.34″N and 116°3′29.20″-116°7′35.51″E. The definition of the scope of Duobao Sand Hill can provide a unified reference range for the subsequent planning, governance, and research of Duobao Sand Hill.

This paper presents a deep learning based method for inversion of Aerosol Optical Depth (AOD) from Landsat 8 OLI multi-band satellite data. Traditional aerosol inversion algorithms include the dark target method, the deep blue algorithm and other algorithms. The dark target method is good for areas with low surface reflectivity such as dense vegetation and water bodies, while the deep blue algorithm is usually used for inversion in areas with high reflectivity. Therefore, this study employs Deep belief networks (DBN) to learn the potential relationship between Landsat 8 OLI multi-band data and AOD. In this paper, Landsat 8 OLI remote sensing images with atmospheric AOD observations over the past 6 years (2013-2018) were collected as the training dataset. A deep confidence network structure is next designed for learning the mapping relationships from Landsat 8 OLI multi-band data to AOD. To evaluate the performance of the proposed method, we used a test set for validation. The experimental results show that the proposed deep learning-based method has higher accuracy and stability in AOD inversion compared to traditional methods.

Mangroves are of great significance to maintain the balance of human production, life, and the natural environment in coastal areas, and Beibu Gulf is an important mangrove concentrated area in China. In this paper, the Beibu Gulf of Guangxi is taken as the research area. With the support of Google Earth Engine (GEE) remote sensing big data cloud platform, Sentinel-2 MSI and Sentinel-1 SAR data in 2022 are used as data sources. According to spectral characteristics, remote sensing index, phenological characteristics, backscattering characteristics, and texture characteristics, a multisource data set is constructed. The mangrove remote sensing recognition model is constructed by using a random forest algorithm, and the mangrove extraction accuracy of the five models is compared to determine the optimal model. The results show that the classification scheme combining active remote sensing data, optical and SAR remote sensing indices, texture, and phenological characteristics achieves the highest extraction accuracy, and the overall accuracy and Kappa coefficient are 96.23 % and 0.934, respectively. It shows that the texture features and phenological characteristics in mangrove distribution extraction are helpful to improve the accuracy of mangrove extraction. This study verified the feasibility of remote sensing mangrove extraction using phenological characteristics, and the proposed method has certain reference value for mangrove monitoring in the Beibu Gulf region.

A variety of complex geological disasters induced by extreme rainfall seriously threaten people's production and life safety, among which landslide disasters are the most widely distributed and the most destructive. In this paper, the typical rainfall concentration area of Guangdong Province (Qingxin-Qingcheng-Fogang-Yingde) is taken as the research area. Based on multi-source data, four machine learning methods are used to calculate the vulnerability of geological disasters such as landslides induced by heavy rain and flood, and the importance of the characteristic factors is analyzed to complete the landslide hazard susceptibility map. On this basis, the SINMAP model is coupled to enrich the data samples, improve the generalization ability of the machine learning model, and provide theoretical support for the risk prevention and resistance in the disaster situation of Guangdong Province. The results show that XGBoost model has the best performance, and its AUC value is 0.90, which is the largest among the four machine learning models. Among the 16 features, elevation contributes the most to the occurrence of landslide disasters, accounting for 15%; In XGBoost-SINMAP coupling model, the accuracy rate is 89%, the precision rate is 87%, and the recall rate is 92%, which further improves the prediction performance of XGBoost model.

The Greenland Ice Sheet accounts for 10% of the world's total ice volume, and if all of it melts, it will lead to a global sea level rise of 6 to 7m. As the world's second-largest continental ice sheet, it is surrounded by many overflowing glaciers, whose melting causes more ice to be injected into the sea. Therefore, in order to carry out the dynamic monitoring of the surface motion of the overflow glacier, this paper uses Sentinel-1A as the data source and Petermann Glacier in NW Greenland as the study area, and the offset tracking algorithm has been used to obtain information on the distribution of surface flow velocity of the glacier in 2021.Meanwhile, the results of ice flow rate in odd-numbered months in 2021 are taken as an example, and the ice flow rate monitoring results in this paper are verified to be highly reliable by comparing them with the publicly available ice flow rate data on the official website. Finally, the ice flow velocity was analysed in the direction of the 2 profile lines along the flow sample zone and the cross-flow sample zone, and the trend of ice flow velocity showed high consistency overall.

There has been a good foundation for the development of China's geographic information industry, but it still faces great challenges in focusing on the integration of the new generation of geographic information technology and geological survey business. By comparing the process of geo-information technology in the United States to change geological survey, this paper has systematically analyzed the 4 problems faced by China's geographic information industry under the background of a new round of strategic actions for prospecting breakthroughs, as well as the basic principles for the application of geographic information technology. Through in-depth study, the three trends in the integration of the new generation of geographic information technology and geological survey business have been pointed out. The paper believes that geographic information technology has a broad application prospect in geological prospecting, which provides technical support, services and guidance in intelligent prospecting and big data, and can greatly improve the efficiency of geological prospecting.

Extreme weather events have increased in frequency as a result of recent, more severe global warming. Several Chinese cities, including Chongqing, were in "baking" mode in the summer of 2022 due to the intense heat waves that swept across the northern hemisphere. In this study, the main urban area of Chongqing is chosen as the research object. Field research and online questionnaire interviews are used to determine the risk assessment index of high-temperature disaster-causing factors, the sensitivity assessment index of disaster-preventing environments, and the vulnerability assessment index of the disaster-bearing body. A high-temperature disaster assessment model is created in light of these findings. The results of each district's resilience assessment are then computed, and the results of factors that can cause high-temperature disasters are combined to produce the disaster risk assessment. The results of each district's resilience assessment are compared horizontally and vertically in order to provide a strategy for boosting Chongqing's resilience in the face of high-temperature disasters.

Peer-to-peer accommodations have experienced rapid development in Chinese cities, which plays an vital role in the urban tourism. This study aims to explore their spatial distribution and identify influencing factors of peer-to-peer accommodations in Beijing using GIS and geodetector. The results reveals an agglomeration pattern of accommodations characterized by denser concentrations in the northeast and sparser distributions in the southwest. Dongcheng district serves as the primary agglomeration core, with the accommodation density gradually decreasing outward from multiple cores. Over time, the overall distribution of accommodations has shifted from the northeast to the southwest, transitioning from core aggregation to a more regionally balanced distribution. Multiple factors, including proximity to attractions, availability of infrastructure, user sentiment, number of reviews, listing scores, road network density, traffic location, and price, contribute to the observed pattern of accommodation distribution

Site selection is a complex task, especially in mountainous areas, where more factors about estimating site suitability need to be considered. The choose of evaluation indicators and the setting of indicator weights have a crucial impact on the site selection results. Panzhihua city has typical mountainous characteristics, from which some villages are drawn as the research data. Firstly, eight evaluation indicators are screened out through literature analysis; then the indicator values of these villages or towns were calculated using GIS method and statistical method. Decision tree model is adopted for evaluation suitability level of each village in this paper. Unlike those methods of directly calculating the evaluation score, the indicator weights in the decision tree model needs to be learned in advance, and then new candidate sites can be estimated by these learned rules. In addition, a new method is proposed for computing the pair-wise matrix, in that the gini coefficient is used to calculate the relative weights of two indicators each other. Through the test, the recall and precision of the trained model are 0.89. This model is also used to predict new sites.

There are several discrimination techniques of foggy days for processing heavy fog data at home and abroad, but their applicability in Leizhou Peninsula is still unclear. Based on the hourly average observation data of 11 automatic meteorological observation stations in Maoming and Zhanjiang regions in the north, middle and south of Leizhou Peninsula from 2011 to 2020, this paper compares the applicability of three foggy days discrimination techniques, namely, daily average visibility method, 14:00 visibility method and visibility duration method, in Leizhou Peninsula, and selects the best discrimination technique to analyze the spatial-temporal distribution characteristics of heavy foggy days. The results show that the visibility duration method has the highest applicability in Leizhou Peninsula, and is the most accurate for judging the heavy foggy days. Using the method of visibility duration to judge the number of foggy days, it is found that the average foggy days in Gaozhou area in the south of Leizhou Peninsula are 26 days. The seasonal distribution of foggy days in Gaozhou is generally uniform, with the highest number of foggy days in May being 20 days. The spatial distribution of foggy days in Leizhou Peninsula is characterized by the maximum number of average foggy days at observation stations in coastal areas, and the closer the station is to the inland, the fewer foggy days it will have.

Winter wheat is one of the major food crops in China, and timely and accurate access to winter wheat planting distribution information is important for the growth detection and yield estimation of winter wheat. In this paper, Henan Province was selected as the study area, and the MODIS time-series EVI data were smoothed and filtered by S-G filtering method, while a decision tree extraction model under different municipalities was constructed by combining winter wheat phenology calendar to extract the winter wheat planting area and spatial distribution in Henan Province in 2018. The experimental results show that: the wheat planting area in the eastern part of Henan Province is more than the wheat planting area in the western part; compared with the statistical data, the extraction accuracy of winter wheat remote sensing in each city ranges from 81% to 96%, and the extraction accuracy of the province reaches 93.94%; the overall average accuracy of MODIS extraction results is 78.43% compared with the extraction results of Sentinel-2. The experiment proved the effectiveness of the method in this paper, which can provide reference and reference for the formulation of grain planting plan and related planting policies in Henan Province.

How climate changes affect the landscape patterns, which has important guiding significance for understanding the mechanism of climate changes influencing the wetland ecosystem. In this study, climate data and image data, during1980-2014, were used to analyze the impact of climate changes on the wetland landscape patterns from the perspective of climate mutation and different combinations of water and heat climate models. The results have showed that: (1) From 1980 to 2014, the climate changes had tended to turn humid and hot in Nansi Lake. The spring warming has been the greatest contribution to the increase of annual average temperature. (2) From 1984 to 2014, the natural wetland area of Nansi Lake Wetland had decreased year by year.(3) Climate changes have affected the transfer modes and transfer rates among wetland landscape types, especially affecting the dominant wetland landscape types. Climate changes have also promoted the landscape complexity and fragmentation of Nansi Lake wetland.

This paper used SPSS statistical analysis and GIS spatial analysis to construct an analytical framework based on urban traffic crime data. The case location record of dangerous driving offences, traffic accident crimes and offences against public safety from 2016-2020 on the China Judgements Online was used as the data foundation. Taking an empirical study further interpreted the distribution characteristics and influencing factors of the traffic crime phenomenon in the central city of Wuhan. The findings were as follows in the central city of Wuhan from 2016 to 2020. (1) The number of traffic crimes showed relatively obvious characteristics in terms of temporal differences. The fluctuations of the yearly, monthly and hourly data were the most obvious, while the fluctuations of the daily data were more random. (2) The overall spatial distribution of traffic crime points showed a dispersion process to agglomeration yearly by using average nearest neighbour analysis, but the degree of aggregation was not high. Spatial autocorrelation analysis showed that the positive spatial correlation of crime points was relatively apparent, but the difference in clustering local crime points was slight. (3) The kernel density estimation results indicated that traffic crime generally showed an elliptical trend in the northeast-southwest direction. The distribution centre of traffic crime points shifted from Wuhan Wuchang to Wuhan Hankou and Wuhan Hanyang. In addition, crime hotspots and repeat crime hotspots indicated a situation of high hotspots in the Yangchunhu area of Wuhan Qingshan and sub-high hotspots in the Wangjiawan area of Wuhan Hanyang. It was influenced by factors from the industrial region's layout, crowding and traffic safety on the river bridge. (4) The causes of traffic crime included the gender of traffic crime subjects, the type of vehicle and the origin of the vehicle's place. The gender of drivers, the number of private cars, and the type of accident-prone vehicles had an impact on traffic crime. Moreover, the crime hotspots showed a more obvious consistency with the degree of traffic road integration, but the correlation with traffic road complexity was relatively insignificant.

The study of urban employment spatial characteristics using static statistical data often has limitations in terms of time and spatial scales, making it difficult to obtain accurate results. This paper used Shen Yang city as a case, space-time behavior information of urban residents was analyzed deeply based on cell phone signaling data. The spatial distribution of employment and job-housing connection data was extracted by establishing data cleaning rules of place of residence and employment identification. Use the concept of employment centers proposed by McMillen (2001) as the theoretical basis, we established of an analytical model. Employment centers was identified with the tecnologies of nuclear density, spatial autocorrelation, natural discontinuous classification and contour line, and we analyzed the spatial distribution characteristics of the employment centers. We have also constructing the hierarchy system of employment centers, through measuring the service efficiency of the employment centers from employment scale and function connection. Based on the above research, we have explored the spatial structure of urban employment, and revealed the internal mechanism of urban operation.

The monitoring of grassland resources is an important part of grassland ecological monitoring and management, aiming to understand the basic resources of grassland and construct a "one-map" of grassland resource management, so as to facilitate the comprehensive evaluation of the current status and characteristics of grassland resources and provide strong data support for subsequent work such as scientific environmental protection, restoration, utilization and management. Therefore, grassland resource monitoring must be regarded as a fundamental task. This paper focuses on the problem of forest-grass classification in remote sensing images, and proposes a deep learning algorithm be called CBAM + DeepLab V3+ for accurate classification of vegetation, combined with the method of fine-grained image classification, which improves its semantic segmentation effect in remote sensing image, increasing the mIOU from 62.80% to 63.25%, and is able to distinguish forest and grassland more accurately than traditional DeepLab V3+.

Naked-eye 3D map is based on the theories of DEM (Digital Elevation Model) and lentricular grating imaging，which users can get three-dimensional experience without the aid of software and hardware systems. The hierarchical method on depth of field and plane displacement are the key techniques and used to reduce the production cost. This article briefly introduces the three-dimensional display principle of Naked-eye 3D maps, elaborates on the production process of Naked-eye 3D maps, and finally summarizes the printing and post-printing processing methods of Naked-eye 3D maps.

The planning and design of river course, reclamation of wasteland and landscape transformation all need to be supported by accurate and detailed current data. As it involves the installation of water conservancy facilities and the laying of pipelines, the accuracy of elevation information is particularly important. In the past, it is time-consuming and laborious to collect cross section and topographic data in the whole field. The airborne laser radar of unmanned aerial vehicle has been applied in river embankment survey because of its high automation degree, short data production cycle, high precision and little weather influence. In order to obtain the digital elevation model data of the river embankment, this paper proposes to use the laser radar system of UAV to measure the river embankment. The plane accuracy, elevation accuracy and DEM accuracy of point cloud data are analyzed by combining with practical engineering cases, and the quality results of these three aspects are analyzed. The results show that the accuracy of the results can meet the requirements of relevant specifications, which provides an important reference for the application of airborne Lidar in river embankment survey.

Accurate extraction of cement plants is important for the regulation of polluting enterprises and environmental protection. Conventional cement plant identification methods are characterized by low efficiency, high cost, and limitations incomprehensive and precise monitoring. Considering the successful application of deep learning in visual object detection, this research presents a modified Faster R-CNN network tailored specifically for detecting cement plants in remote sensing images. Our approach utilizes a multi-level fusion structure, integrating deep semantic features with more superficial detail features. This combination yields multi-scale feature maps that provide precise positional data along with deep semantic context. The experimental results demonstrate that our proposed method effectively detects multi- scale cement factory targets in remote sensing images, reducing the omission rate and improving target localization accuracy.

In view of the complexity and low accuracy of traditional sea surface salinity inversion models, this study took the Bay of Bengal sea area (82°~91°E, 9°~15°N) as the research area, and used the L-band sounding SMOS Level2 satellite product data, Argo measured data and characteristic data. Catboost algorithm was used to construct the remote sensing inversion model of sea surface salinity, and grid search algorithm was used to optimize the model parameters, so as to improve the accuracy of SMOS satellite products. The validation results show that the root mean square error (RMSE) of the Catboost model's test set inversion of SSS is 0.2929psu, and the mean absolute error (MAE) is 0.1885psu. Comparison and analysis using measured data show that the Catboost model's inversion of SSS has a high degree of consistency with Argo data. The validation also analyzed the RMSE errors between the model's inversion results and the Argo measured results for each month in 2021, revealing the potential relationship between climate and meteorological variables in the Bay of Bengal and changes in salinity.

With the continuous development of UAV technology, aerial photography has become an important means to obtain geographic information. With the wide application of aerial photography image, the evaluation of its quality is becoming more and more important. In the absence of reference images, the image quality comprehensive evaluation model is used to evaluate the image quality without reference. On the basis of selecting each sub-evaluation parameter, the software system puts forward the decision method of comprehensive quality evaluation, and finally gives the comprehensive quality evaluation result. The calculated quality value is consistent with the subjective evaluation. The evaluation results can know the image performance of aerial photography system, the software interface is friendly, stable and practical, has a wide range of application prospects in engineering

The rapid development of unmanned aerial vehicle (UAV) aerial survey technology and deep learning has revolutionized traditional aerial survey methods. With its fast measurement, low cost, good safety, and high reliability, UAVs can quickly obtain image data of a certain survey area in a relatively short time. Although UAV surveying has been used in various fields of related industries, there are still certain limitations to quickly obtaining desired information from a group of images. Therefore, this paper combines the VGG16 deep learning model to match UAV images and make it applicable to the web environment. Starting with extracting image features of UAVs from the VGG16 model, this paper implements a UAV image feature extraction and retrieval system based on VGG16. Then, the matching efficiency is compared with the matching model based on the SIFT algorithm. Finally, the image feature extraction and retrieval system based on VGG16 is combined with the web system to study the retrieval of image data of interested areas or image data without spatial information, as well as spatial information queries under web conditions.

The current large-scale visualization of 3D geographic scenes, such as real-life 3D and smart cities, requires a large number of fine models such as triangulation networks and patches. Conventional engines are difficult to load such large-scale 3D GIS data, and computers require extremely high memory and graphics memory to load the entire scene data, making it difficult for conventional computers to achieve. To solve this problem, this paper adopts the multi-resolution construction method of fine model, uses adaptive node division to improve the spatial data organization of the traditional octree fine model and the task stealing algorithm to improve the existing parallel divide and conquer merge algorithm to build a triangular network, and optimizes the edge collapse simplification algorithm based on improved QEM through multithreading during the model construction process before simplifying the triangular network, To ensure fast transmission in UE5 and efficient rendering on GPU.

Road network system is the main carrier of physical connections such as population and logistics within urban agglomerations, and plays an important role in supporting and guiding the regional development and spatial restructuring. Therefore, it is of great significance to carry out a comprehensive evaluation of the development of road networks and correlation with regional development in urban agglomerations from a spatial perspective. This paper takes the Greater Bay Area of Guangdong, Hong Kong and Macao (GBAGHM) as a case study to conduct a comprehensive evaluation of the development level and spatio-temporal expansion characteristics of the road networks based on geographic information system (GIS). Furthermore, the spatial statistical theories and methods, such as exploratory spatial data analysis, multiscale geographically weighted regression and geographic detector, are used to measure and characterize the spatial correlation between the road networks and regional development, and to reveal its spatial heterogeneity and influencing factors.

Wet natural gas moisture content measurement technology plays an important role in natural gas production, storage and transportation. This paper introduces the commonly used measurement methods of moisture content of wet natural gas: electrical measurement method, ray method, quick closing valve direct measurement method, optical measurement method, acoustic measurement method, microwave method. It is predicted that the future measurement technology will develop in the direction of high precision, speed, intelligence and comprehensive automation, so as to solve the problems existing in the current detection process of various wet natural gas moisture content and improve the measurement level of wet natural gas moisture content.

Measuring of angles in sets is a common method used by the theodolite to measure the horizontal angle. When angle measurement is carried out by the method of measuring of angles in sets, all surveying specifications regulate to presetting the initial readings using the optical theodolite, while most of the surveying specifications do not have such requirement for the electronic theodolite (including the total station), which causes a problem for the instrument users. Most users default to the fact that the electron theodolite does not pre-set the initial readings when measure angles in sets, but there is no theoretical basis, which should be solved theoretically. In this paper, the angle measurement in sets using electronic theodolites is carried out in two ways: with pre-setting the initial reading and without pre-setting the initial reading, and the obtained data are analyzed by hypotheses test method. The results show that there is no obvious difference between the two methods of angular measurement of the same electronic theodolite. Therefore, it is recommended that when electronic theodolites are used in angle measurement in sets, there is no need to pre-setting the initial readings. Thus, makes the field work efficiently.

Large-scale remote sensing images contain rich terrain information, while small-scale images capture abundant detail information. Effectively extracting and fusing these multi-scale information has grown into a major challenge. To tackle these problems, this paper recommends a novel remote sensing image segmentation model called Multi-scale Vision Transformer (MS-ViT). The MS-ViT model comprises a multi-scale feature extraction module, a feature fusion module based on multi-scale self-attention, and a decoder based on convolutional neural networks. Additionally, the model introduces spatial attention mechanism and a position encoding method considering patch sizes, as well as a feature distribution alignment method and corresponding loss functions to further enhance segmentation performance. Experimental results demonstrate significant improvements in performance of the MS-ViT model compared to several other advanced models.

Building is one of the important contents of map data. The recognition of the spatial distribution patterns of buildings is the basis and key step of the automatic cartographic synthesis. It is not only important for the implementation and quality control of automatic cartography, but also the study of spatial object distribution characteristics and distribution patterns is one of the basic tasks of geographic information science. In this paper, the grid distribution pattern recognition method of buildings based on a graph convolutional neural network model is studied by selecting seven feature description factors. The experimental results show that this method can effectively recognize the grid distribution pattern of buildings.

Increased coverage of impervious surfaces is an important measure of urban sprawl, and understanding spatiotemporal dynamics of impervious surfaces is vital for regional and local development. Combining vegetation-impervious-surfaces oil model and linear spectral unmixing model, this paper used the Landsat TM and OLI remote sensing data from 2009 to 2019 to study the temporal and spatial variation characteristics of the impervious surfaces of Zhengzhou, and discussed the main driving factors leading to urban expansion. We found that impervious surface growth in the main urban area of Zhengzhou City was obvious, and the area of impervious surfaces increased from 337.52 km² in 2009 to 464.93 km² in 2019, with an average expansion speed of 12.69 km² /year. The impervious surfaces pattern of the research area changed significantly, and the expansion of impervious surface showed a flaky development in all directions. From 2009 to 2014, Zhengzhou’s expansion was mainly concentrated in Hi-Tech Industrial Development Zone, Zhongyuan District, as well as Economic and Technological Development Zone. Most of the expansion between 2014 and 2019 was concentrated in Zhengdong New District and Huiji District. Physical geography, urban population, GDP and national policies were the main drivers affecting the expansion of impervious surfaces.

Qingdao is an important coastal city in China, and there are a large number of dense buildings, factories, and ports distributed in the coastal area, and they promote the economic development. In this article, the long-term series monitoring of land subsidence is carried out using SBAS-InSAR technology based on 47 Sentinel-1B image data covering the Qingdao West Coast New Area. The results show that the overall surface deformation of Qingdao West Coast New Area is relatively small and stable. The deformation of the coastal land reclamation area is obvious. The maximum vertical deformation rates of three typical subsidence areas, i.e., Qingdao Sunac Victoria Bay, Xingangshan Road Station of Metro Line 1, and Xingguang Island, are -63.7mm/y, -42mm/y, and -22mm/y, respectively. According to the analysis，the unstable geological conditions in the early stage of land reclamation are the main reasons for the subsidence. But with the passage of time, engineering construction and building load have become more important factors affecting the land subsidence of the Qingdao West Coast New Area.

In the urban renewal context, the leftover spaces in the city need to be uncovered and utilized. However, the existing studies on urban leftover spaces are still inadequate, particularly in the construction of leftover spaces. These studies are mainly based on qualitative and theoretical suggestions, lacking quantitative index-oriented decision-making suggestions. With the extensive integration of the Rhino + Grasshopper parametric platform with urban design, the application of such techniques to the identification and evaluation of leftover spaces could be conducted. Given the above research gap, this study used the parametric platform to measure the spatial type, pedestrian flow potential, and thermal comfort of leftover spaces to identify potential leftover spaces. Then, this study provided specific design recommendations for their spatial construction based on multiple leftover spaces evaluation indicators. The model constructed in this study is helpful to excavate urban leftover spaces with utilization potential at a small spatial scale, such as a block. This model can also provide a scientific design assistance reference, which is an effective tool to improve urban land use efficiency and urban space quality.

With the rapid development of virtual reality as well as geographic information technology, panoramic technology has achieved wide application. For the problems of slow loading speed of large panorama and the lack of measurement function of 2D images, this paper proposes a multi-level pyramid model based on spherical projection. Firstly, the 3D laser point cloud data is transformed by coordinates and projection to generate the depth image corresponding to the panorama, and the 3D application value of the panorama is explored; then, the panoramic images are compressed and sliced to form multi-level panoramic tiles and construct a grid sphere model; finally, the 2D panoramic image is mapped into the 3D grid sphere for rendering according to the vertex coordinates, and slices within the corresponding range are loaded according to the viewing area during browsing. The experimental results indicate that the sliced model significantly improves loading and display speed compared to loading the entire panoramic image, which has strong application value.

In order to explore the impact of water resources development and utilization on the ecological environment of the Taohe River Basin and coordinate the relationship between water energy development and ecological environment protection, this paper analyzes the impact of water resources development and utilization on the ecological environment of the Taohe River Basin by using the data of runoff, sediment, land use types and remote sensing ecological index for many years. The results show that: 1) After the construction of hydropower station, the proportion of runoff in flood season is relatively reduced, while the proportion in non-flood season is relatively increased; the water quantity decreased in summer and autumn and increased in winter. 2) The silt of Taohe River mainly comes from flood period, and the sediment transport in summer is the largest, up to 82.26% of the annual sediment transport; the sediment transport showed a decreasing trend from 1991 to 2021. 3) During the 40 years from 1980 to 2020, the transfer area of grassland, forest land and cultivated land in the Taohe River Basin has been changed to some extent, but the small difference between the area transferred in and the area transferred out, resulting in the overall change of land use types area in the study area during this period was not large. 4) The RSEI ecological index in the Taohe River Basin from 1990 to 2020 was at a medium level, but showed an overall upward trend; the spatial distribution shows the pattern of "upstream is better than downstream", and the areas with poor environmental quality are concentrated in the downstream areas.

TDOA is a common passive location measurement. Considering that the observation error of the platform will have a greater impact on the accuracy of passive location, a linear filtering method to improve the TDOA estimation accuracy of single target is proposed. When there are multiple targets in the observation area, the geometric relationship between the platform and the target can be avoided by multi-target sorting through time difference. This method can also be used to improve the prediction accuracy in multi-target data association and reduce the number of multi-target detection. Finally, the center point filtering method is compared with Kalman filtering and improved algorithm on the simulation data to verify the accuracy and effectiveness of this method.

Due to the problem of the heterogeneous nature of ocean eddy data sets and the difficulty of expressing their spatio-temporal processes, there have been no satisfactory tools for integrated ocean eddy data management and spatio-temporal process analysis so far. Based on the semantic analysis idea of ontology, this paper proposes and constructs an ontology model of ocean eddies that provides a normative framework for organizing ocean eddy data based on the five-tuple O=(C,T,S,P,R) to solve the problem of heterogeneous sources and heterogeneity of data collection. For the continuous gradual change process of eddies, this paper constructs an evaluation system of ocean eddy development stages based on the entropy weight method, and provides a more accurate numerical analysis of the eddy evolution process through evaluation indexes. Finally, under the constraints of the above model, this paper proposes and constructs a spatio-temporal object data model of the ocean eddies, and maps the eddies into a graphical database based on "nodes" and "relations", which provides a new idea for the data organization and spatio-temporal process expression of the ocean eddies. The research results show that this strategy can effectively solve the heterogeneous problem of ocean eddy data and improve the efficiency of storage, retrieval and fusion of eddy data in different development stages, and provide reference for the integration and sharing of ocean eddy data and tracking and prediction.

Based on the complex problem of roadway modeling, the characteristic points of each section of roadway are calculated according to the roadway section data and roadway centerline data, and the curve interpolation and cubic Bezier curve interpolation methods are used to provide a practical calculation method for straight roadway and curved roadway, so as to realize the modeling of roadway under different conditions.

Sea ice, as a distinct geographical feature, exhibits both aggregated distribution in large ice floes and scattered distribution in small ice fragments within the ocean. Traditional semantic segmentation networks face challenges in accurately extracting sea ice, including imprecise edge extraction and the tendency to overlook fine ice fragments. To address these issues, we propose a novel sea ice extraction model called CA-DeepLabV3+ that incorporates a coordinate attention mechanism. By integrating the coordinate attention module after the ASPP module in DeepLabV3+, our model effectively enhances the feature representation of both channel and spatial dimensions, facilitating the capture of large-scale information and edge details of sea ice. Experimental evaluations are conducted using Sentinel-1 remote sensing imagery collected from the Greenland Sea in the Arctic. A dataset is prepared through remote sensing data processing, annotation, and augmentation. The performance of CA-DeepLabV3+ is compared with that of classical semantic segmentation networks, including UNet, PSPNet, and the original DeepLabV3+. The results demonstrate that CA-DeepLabV3+ achieves an mIoU of 78.10% and an mPA (mean Pixel Accuracy) of 91.16%. Compared to the original DeepLabV3+, the mIoU is improved by 5.27% and the mPA is improved by 1.29%. Moreover, CA-DeepLabV3+ outperforms UNet and PSPNet, achieving the best performance among all the compared networks. It exhibits stronger learning capability for sea ice features, enabling the acquisition of more detailed information, thus providing further technical support for studying sea ice degradation under global warming conditions.

Addressing the inefficiencies of conventional stripmap Synthetic Aperture Radar (SAR) imaging in scenarios where the target area does not align with the satellite ground track, this paper introduces a novel approach, the distance scanning off-axis stripmap imaging mode. This innovative mode dynamically adjusts the slant angle, Pulse Repetition Frequency (PRF), and start of sampling to adapt to complex imaging scenarios, thereby enhancing imaging efficiency and reducing imaging time. The paper also presents a comprehensive SAR system imaging process flowchart, which includes the computation of nadir satellite parameters, analysis of scene inclination angle variation, and definition of the sampling start rule. By employing a block variable PRF, the proposed mode effectively mitigates the issues associated with large range migration in conventional stripmap imaging. The effectiveness of the proposed mode is validated through imaging simulations. The results underscore its potential to enhance the performance of spaceborne SAR systems, particularly in scenarios where the target area does not align with the satellite ground track

In recent years, remote sensing, computer vision, and other methods have actively promoted the development of threedimensional (3D) reconstruction and automated measurement technologies for power grid systems. Currently, parameters such as tower height heavily rely on manual measurements, which suffer from drawbacks such as low efficiency. To address this, an automated method leveraging 3D point cloud technology for measuring transmission tower height is proposed. Firstly, the application of the PointNet++ model for segmenting 3D point clouds in transmission corridors is introduced, where the point clouds are classified into four categories: transmission lines, towers, ground, and ground wires. Subsequently, using the automatically extracted point cloud data of power towers and ground after semantic segmentation, along with relevant indicators, a prediction model for transmission tower height is constructed based on Support Vector Regression (SVR). Finally, the model is tested on a collection of 3D point cloud samples from transmission corridors. Experimental results demonstrate that the proposed automated method for calculating tower heights achieves a Mean Absolute Percentage Error (MAPE) of 5.86% and enables accurate estimation of transmission tower heights.

This paper takes part of Wulian County, Rizhao City, Shandong Province as the study area, and utilizes UAV image data for land use classification research. The object-oriented random forest (RF) algorithm, logistic regression (LR) algorithm and support vector machine (SVM) algorithm combined with spectral features, index features and texture features are used to classify the land use and compare the classification effects of different algorithms. The results show that the object-oriented random forest algorithm performs better than the other algorithms, achieving an overall accuracyof89.74% and a Kappa coefficient of 0.872. The combination of object-oriented and machine learning methods can be effective for land use classification, and the classification accuracy is also higher.

In order to cope with the special conditions of hard soil and large soil cutting volume in autonomous slope repair operations, the existing autonomous slope repair process is improved, and a real-time monitoring and feedback system is proposed to be built for the excavator using binocular vision. The system includes the estimation of the total amount of soil cut in the slope repair project, the real-time estimation of the amount of soil cut and the real-time judgement of the slope flatness. After experimental verification, the height error of the binocular camera on the slope detection can be controlled within 3cm, the distance error between the upper edge of the soil pile and the camera is within 2cm, and the error between the estimation of the volume of soil before and after the construction and the actual measurement is within 0.02m³, which meets the accuracy requirements of the autonomous slope repair construction. It meets the accuracy requirements of the slope repair construction.

In the detection of nearshore ships in polarimetric SAR (PolSAR) images, detecting ship targets becomes challenging when they are densely distributed or located in areas with strong sea clutter scattering. To achieve adaptive and accurate detection of nearshore ship targets in PolSAR images, this paper proposes an adaptive superpixel-level CFAR detection method based on the improved geometric perturbation polarimetric notch filter (GP-PNF). Firstly, the entire image is segmented into superpixels using the polarimetric SLIC method, and sea-land segmentation is performed utilizing the OTSU method. By employing an automatic censoring (AC) mechanism, superpixels containing pure clutter are selected to estimate the parameters. Following this, the improved GP-PNF is utilized to construct the statistical metric for detection. Finally, the detection results are obtained based on a new superpixel-level CFAR strategy and subsequent post-processing. Experimental results based on GF-3 full-polarization SAR data demonstrate that the proposed method is capable of accurately detecting nearshore ship targets in PolSAR images.

Synthetic aperture radar (SAR) has been widely applied in the remote sensing field. SAR compensates for the shortcomings of infrared and optics remote sensing, which can observe targets regardless of time and weather. The interpretation of SAR images has always been a research hotspot. Aircraft are an important military and transportation target. Rapid detection and recognition of aircraft targets are significant to combat decision-making and civil aviation dispatching. The research content of this paper is to detect and recognize the aircraft target in SAR image. With the rapid development of computer vision and deep learning theory, this paper proposed an improved algorithm based on YOLOv5s. Firstly, multi methods of data augmentation (Data-Aug) are used in our algorithm to enhance the diversity of the dataset and generalization of the trained model. Secondly, this paper proposed the Ghost structure to optimize the YOLOv5 network to decrease the parameters of the model and the amount of computation. Thirdly, the convolutional block attention module (CBAM) is incorporated into the backbone section of YOLOv5 to help extract useful information. Finally, a series of experiments showed that the proposed algorithm performed better without affecting the calculation speed.

The thesis adopts industrial concentration index, city industry transfer undertaking index and other analytical methods to analyze the spatial and temporal characteristics of 84 cities above prefecture level in western China in undertaking manufacturing transfer, and the study shows that: (1) There are 15 manufacturing center cities and about 10 cities with manufacturing external service functions in the west, gathering about 8.1%-9.3% of the total urban manufacturing population in the country; (2) Between 2010 and 2019, the overall change in manufacturing center cities in the western region was relatively small, and the number of cities transferring in manufacturing increased, but more than half of the cities were those transferring out of manufacturing, indicating that the growth rate of the manufacturing population in the west was generally lower than that in the eastern and central regions. (3) There is an obvious geospatial proximity effect for cities in the western region to undertake the transfer of manufacturing, and the hotspot areas in the western region to undertake the transfer of manufacturing has changed significantly during 2010-2019.

The construction of three-dimensional models of buildings is a significant aspect of smart city development. This paper focuses on achieving high-precision fusion of outdoor three-dimensional models and indoor BIM (Building Information Modeling) models of buildings. To accomplish this, a technical process and method for the geometric space registration of indoor and outdoor three-dimensional models of buildings are proposed, utilizing the fusion of UAV (Unmanned Aerial Vehicle) oblique photography and BIM models. Additionally, a three-dimensional space similarity transformation module is developed using the open-source secondary development tool, Skyline. The experiments conducted demonstrate that the proposed method enables automatic geometric registration of the UAV oblique photography building outdoor three-dimensional model and BIM indoor three-dimensional building model. This approach overcomes the limitations of traditional indoor and outdoor three-dimensional model fusion, which lacked an automatic and high-precision geometric registration method.

With the rapid development of surveying and mapping equipment and technology in the new situation, How to implement the information-based surveying and mapping production of major national projects more efficiently has become an important research content nowadays. This paper analyzed the key technologies of data pre-processing, standardized processing and quality inspection in production. It proposed intelligent solution for batch processing, computer discrimination, etc. Designed and developed the software system which covered the whole production. Verified by some major surveying and mapping production test, this technology roadmap is close to reality and completely penetrates every procedure of the production. It can improve the efficiency of production commendably, and has good practical application value. It has some reference significance for the new basic surveying and mapping which will be rolled out in full scale soon.

The algorithm of extracting skylines are mostly two-dimensional image processing methods, which are lack of geographic information in current. This paper, a skyline extraction algorithm based on airborne LiDAR point cloud data is proposed. Through the optimization of the EDL algorithm, the shading function is calculated using the projected depth data and applied to color scene images to synthesize a more three-dimensional and realistic point cloud. Through the application in the laser scanning project of Wuhan city, the skyline of key urban areas was extracted and made into a thematic atlas. The results showed that the algorithm can quickly extract the skyline, and the quality of the results meets the requirements.

Since 2008, straw burning has been vigorously banned in China. To investigate the impact of straw burning on AOD, this study used VIIRS nominal and high confidence fire point data and MODIS cropland land cover data to identify straw burning fire points. Additionally, by utilizing MOD04_3K aerosol products, the study analyzed the spatiotemporal correlation between straw burning during the summer and autumn harvest seasons and the aerosol optical deep (AOD) from 2012 to 2021. The results show that: (1) The distribution of straw burning fire points and ADO in Shandong Province exhibits significant seasonal characteristics, with AOD being heavily influenced by seasonal variations. (2) There is a linear relationship between the number of straw burning fire points and the seasonal mean AOD, with a correlation coefficient of 0.8145 in summer and 0.8907 in autumn. The correlation in autumn is slightly higher than that in summer. (3) The distribution of fire points coincides with the high AOD areas, mainly concentrated in the northern, western, southern, and central-eastern parts of Shandong Province, which are primarily wheat and corn planting regions. (4) The AOD in Shandong province showed a decreasing trend during the decade, and it was more obvious in the northern, western and central-eastern regions. These areas have the same spatial distribution as the regions where straw burning has decreased.

A vector data overlay analysis algorithm based on spatial partitioning is proposed for the demand of efficient vector data overlay analysis service. By establishing GiST index and aggregation, it makes the storage of graphic data more centralized and reduces the number of queries in the query scanning process, and by splitting the polysemy surface, the external rectangle range of the whole surface can be reduced, which in turn reduces the number of calculations in the superposition analysis process, and cuts the complex single-meaning surface into a smaller and more concise form. The outer rectangles of the cut single-meaning surface can be used as index correlation conditions, which can compress a large number of invalid query ranges, thus improving the analysis efficiency when overlaying complex faceted graphics with thematic land resource data layers. The experimental results show that the query return time after the optimization of overlay analysis is reduced by nearly 70% compared with that before the optimization, which greatly improves the performance of overlay analysis.

This study introduces a real-time monitoring and analysis system for industrial heat sources. We employed the K-means algorithm and VIIRS ACF fire point data to identify industrial heat source objects. Additionally, thematic maps based on ARCGIS were generated, and users were notified via email using an SMTP server. By analyzing thematic maps from January to April 2023, we obtained a comprehensive understanding of the quantity and distribution of industrial heat sources in Hangzhou City. Our approach can be extended to other regions, providing a novel method for real-time monitoring that contributes to environmental protection and sustainable development.

Hyperspectral imaging payloads are widely employed in diverse applications, including geological surveys, plant research, resource exploration, and more. Despite the emergence of spaceborne hyperspectral imaging, airborne imagers remain crucial in remote sensing due to their superior performance capabilities. This article offers a comprehensive review of hyperspectral imaging payload development and application across various platforms and instruments, with a specific focus on the limitations of unmanned aerial vehicle-borne platforms. Ultimately, this research points out there are several challenges that need to be addressed in future studies and presents an outlook of challenges and future trends. This article also assists researchers in quickly gaining an overview of the existing vast literature in the related fields.

In order to expand the adaptability of the microwave radiometer (MWR) to the marine environment, the MWR tropospheric temperature and humidity profile system for the marine environment was developed. An idea of retrieving the 0~10km temperature and humidity profile by integrating the atmospheric experience database and the differential evolution algorithm (DE) was proposed. Through a large number of temperature and humidity profile simulation calculations, the feasibility of the algorithm was finally proved. The simulation results show that the root mean squared error (RMSE) of temperature profile retrieved by DE is 4.3K, the mean absolute error (MAE) is 3.4K, the RMSE of humidity retrieved by DE is 22.9%, and the MAE is 19.1%. The algorithm based on the fusion of atmospheric experience database and DE has the characteristics of independent of historical data, which provides a new idea for the marine atmospheric detection lacking of historical data.

To study the patterns of algal blooms in Chaohu Lake, this article uses Sentinel-2 images as the data source and extracts algal bloom using the Normalized Difference Vegetation Index (NDVI). From 2019 to 2022, the dynamic monitoring of algal bloom in Chaohu Lake is conducted, analyzing the temporal and spatial changes in algal blooms, exploring the driving factors of algal blooms in Chaohu Lake. The results indicate that: (1) From 2019 to 2022, the scale of the annual outbreak of algal bloom in Chaohu Lake gradually decreased. The degree of water blooms significantly decreased. (2)Chaohu Lake has strong seasonal changes in water blooms, with severe outbreaks in summer and autumn when temperatures are higher, indicating that temperature is the main factor driving the outbreak of water blooms. (3) As the main pollutant discharge area of Hefei urban area, the northwest of Chaohu Lake is the core area for algal blooms, indicating that water quality is an important influencing factor leading to algal blooms in Chaohu Lake; From the spatial changes in the movement of algal blooms from the northwest and the coast of east lake towards the middle of the lake, and from the middle of the lake to the northwest, it is found that wind speed is more likely to suppress the outbreak of algal blooms.

In this paper, four historical cases of severe drought in different seasons were chosen based on multi-source remote sensing data. Several remote sensing-based drought indices were calculated, and the conditions were uniformly divided into dry and wet based on the drought indices by using ArcGIS technology. The monitoring results using several remote sensing-based drought indices for the four cases were compared against those using SPEI and MCI that were calculated based on measurements at meteorological stations. In this way, the suitability of several remote sensing-based drought indices was assessed for drought monitoring in Southwest China. The results show that no single remote sensing-based drought index can accurately monitor the dry and wet characteristics of the Southwest China with complex topography and diverse climate types, and all of them have certain shortcomings and need to be further improved subsequently.

Global Navigation Satellite System (GNSS) reference station network of Guangxi Province comprises 110 provincial reference stations strategically positioned throughout the entire region, providing high-precision real-time positioning services for users in the region. Through the quality inspection of observation data of 110 reference stations in Guangxi, the service stability of the GNSS reference station network is analyzed. The research findings indicate that the overall quality of observation data from the GNSS reference stations in Guangxi Province meets the required standards, enabling the stable provision of high-precision positioning services to users. Moreover, a detailed analysis of the observation environment of 13 reference stations in Nanning and Fangchenggang has been conducted, providing a solid foundation for the effective operation and maintenance of GNSS reference stations in Guangxi Province.

Three types of formation flight operational schemes are designed. They are combined with the IRI2001 model, to study the effect of ionospheric delay in relative positioning by onboard GPS of space-aircraft. Deep analyses also make for double-differential ionospheric delay of single frequency GPS onboard LEO satellite. Some useful conclusions are drawn for ambiguity solution in carrier phase positioning in such a case that two spacecraft with 1100km high from the Earth and a distance from 150km to 200km. The residuals of double-differential ionospheric delay usually don’t affect the solution of the ambiguity.

In order to reduce the bias between the measured brightness temperature and the simulated brightness temperature, and improve the detection accuracy of the ground-based microwave radiometer, a first-level data quality control and correction model of the QFW-6000 ground-based microwave radiometer was studied, and the BP neural network model was used to reverse Perform atmospheric temperature and humidity profiles. The experimental results show that after the quality control, the correlation between the measured brightness temperature and the simulated brightness temperature of each channel is significantly improved, and the inversion accuracy of the neural network model is improved to a certain extent after the quality control and correction. The established neural network model improves the inversion accuracy, and the root mean square error ranges of atmospheric temperature and relative humidity are 3.3-6.7K and 15%-23%, respectively.

To boost the precision positioning accuracy of the global navigation satellite system (GNSS) and effectively analyze the position information of GNSS, a precision positioning method based on BeiDou Navigation Satellite System/Global Positioning System (BDS/GPS) fusion is proposed. The proposed method utilizes a unified method for spatio-temporal references to adjust the time synchronization between the BDS and GPS, and a precise positioning mathematical model of GNSS based on BDS/GPS fusion is constructed in this paper. The tropospheric delay and ionospheric delay are corrected respectively through the Saastamoinen model and the ionosphere-free linear combination model, and the Earth’s rotation errors are corrected in the earth-fixed coordinate system. By employing Kalman filtering, the mathematical model problem of precise positioning is transformed into a linear problem, and the three-dimensional coordinate fixed solution of the model is solved to achieve precise positioning of the GNSS. The experimental results validate the high reliability of the proposed method for precision positioning. The root mean square (RMS) values in the east-west (E) direction, elevation (U) direction, and north-south (N) direction are 4.60 cm, 3.97 cm, and 13.77 cm, respectively, and the RMS value in three-dimensional positioning is 15.60 cm. It boasts a strong positioning ability and is available to increase the number of visible satellites. By appropriately adjusting the space satellite structure, it is possible to achieve further enhancements in precision positioning accuracy.

To achieve accurate detection and compensation of gross errors, and improve the accuracy of target positioning and tracking, a multi-dimensional gross error positioning method for Global Navigation Satellite System (GNSS) based on Unscented Kalman Filter (UKF) algorithm is proposed. The data measured by GNSS is then subjected to singular spectrum analysis, which enables the decomposition of the data into its constituent components. Through this analysis, significant features of the data measured by GNSS are extracted, allowing for the subsequent reconstruction. By comparing it with the original measurement data to determine its residual sequence, the cross-validation method is utilized to determine the optimal window length and main component fraction parameters. According to the Interquartile Range (IQR) criterion, the detection of gross errors in the data measured by GNSS is achieved, and different types of gross errors are compensated using the UKF algorithm to achieve multi-dimensional gross error positioning. The experimental results show that the optimal window length and main component fraction parameters are 5 and 800 respectively. The effective signal decomposed by this method is consistent with the original measurement data, and the residual signal trend is stable. All gross errors can be detected and compensated, and the deviation between the positioning trajectory and the actual target motion trajectory is small.

Satellite remote sensing, as an important means of disseminating and perceiving spatio-temporal geographic information, is widely used in many fields. The accumulated satellite remote sensing data from various industries has gradually formed Remote Sensing Big Data, and its explosive growth has also brought challenges to various applications. It discusses the big data characteristics of Remote Sensing Big Data, and also the challenges posed by the lack of handling technologies in this paper. Meanwhile, it also discusses the opportunities and key technical issues from cloud computing technology to Remote Sensing Big Data handling, and provides a cloud RSBD solution and platform prototype.

Vector polygon overlay analysis is one of the most basic, crucial and complex analysis methods in geographic information systems (GIS). The method is usually the basic algorithm for the constitutions of many high-level spatial analysis models. With the extensive and in-depth applications of vector spatial data structures based on simple feature models, nontopological vector polygon overlay analysis algorithms have attracted more attention from scholars. Many different types of polygon clipping algorithms used for overlay analysis have emerged. The Vatti algorithm, GreinerHormann algorithm, Liu algorithm and Martinez algorithm are 4 acknowledged algorithms for arbitrary vector polygon clipping with reasonable time consumption. Most previous studies focused on the optimization and improvement methods of a single polygon clipping algorithm and lacked comprehensive analysis and comparison between them based on real spatial vector data. In this paper, the 4 polygon clipping algorithms mentioned above are implemented based on the Geospatial Data Abstraction Library (GDAL)/OpenGIS Simple Features Reference Implementation (OGR) model. The spatial complexity and time consumption of the 4 polygon clipping algorithms are tested and analyzed statistically using 4 different Boolean operations, including the intersection, union, difference and symmetrical difference. Experimental results show that: 1) Regarding the spatial complexity, Liu’s and Vatti’s algorithms are the lowest and the highest, respectively, while Greiner-Hormann’s and Martinez’s algorithms are between the other two. 2) Regarding the time complexity, Vatti’s algorithm is the highest as the square order, Martinez’s algorithm is the linear logarithmic order, and the time complexities of Liu’s and Greiner-Hormann’s algorithms are between the linear order and the square order. 3) Regarding the time consumption of these algorithms, when four Boolean operations are carried out between two polygon layers, Martinez’s algorithm takes the least time when the number of the subject layer polygons reaches 7495 and the number of the clipping layer rectangles exceeds 6400.

The Jvhugeng mining area in the Muli Coal field is situated in the high and cold region of the Qinghai-Tibet Plateau. Years of open-pit coal mining have resulted in severe damage to the surrounding ecological environment. Therefore, it is imperative to conduct mine environmental restoration and treatment in order to achieve the goal of ecological environmental protection. To ensure the safe and efficient implementation of subsequent mine environmental restoration and treatment projects, it is essential to accurately and efficiently identify the geological environmental issues and establish clear restoration and treatment goals. Considering the unique natural conditions and geological environment of the plateau alpine mining area, multi-source technologies, such as satellite remote sensing and UAV remote sensing, are employed as the core components. These technologies are complemented by conventional geological surveying and sampling, as well as testing methods. By fully utilizing the technical characteristics of multi-spectral, INSAR, thermal infrared, and other remote sensing technologies, the identification and monitoring of geological hazards, vegetation destruction, water distribution, abnormally high temperature of residual coal on the surface, and soil control in the Jvhugeng mining area have been undertaken. The overall results of the identification process have been positive, providing a scientific basis for mine environmental restoration and control. This achievement holds great significance in ensuring the safe and efficient development of mine restoration and control projects, as well as achieving the desired effects of ecological restoration and control.

LAI is one of the important input parameters of crop model. LAI data is coupled to crop growth model RS-P-YEC with asynchronous length of 5 days, 10 days and 20 days, and the influence of LAI parameters on yield evaluation of crop model is analyzed. The results show that RS-P-YEC model is sensitive to LAI parameters, and LAI parameters with different time steps have a great impact on the simulation results of the model. From the NPP simulation value of the model, the simulation results of 10 days are most consistent with the crop growth status, and the NPP simulation value in late September is between 450-700gC/m² . The precision of RS-P-YEC model with 10 day step LAI is the highest, and the determination coefficient reaches 0.7229. RS-P-YEC model is used to achieve point-to-surface assessment of the impact of drought catastrophe on crop yield, which provides theoretical basis and technical support for improving the accuracy of disaster loss assessment and food security.

Star sensors, as a high-precision attitude measurement device, are widely used in various Earth orbit satellites and deep space exploration fields. Their detection sensitivity has a significant impact on the measurement accuracy and work efficiency of products. A detection sensitivity method for star sensor based on star point matching was proposed in this paper to address the issue of existing sensitivity calculation methods being limited by the maximum sensitivity of star catalogs within the product, which cannot accurately determine the maximum detection sensitivity of star sensors. Firstly, the position coordinates of the detection star in the image are obtained through the connectivity analysis algorithm. Secondly, the position coordinates of the navigation star in the field of view on the detector image plane are obtained based on quaternions and star sensor camera parameters. Finally, the detection sensitivity of the star sensor is obtained by matching the detection star with the navigation star. The ground observation test results show that the star sensor detection sensitivity analysis method based on star point matching proposed in this paper can obtain the detection sensitivity of the star sensor accurately and effectively, and it has practical application value.

Climate change is a common problem facing human society, and carbon emissions aggravate global warming. This paper takes Fujian Province as the research area. DMSP/OLS and NPP/VIIRS data were preprocessed, corrected and fused to generate and grow time series data sets. Through the construction of carbon emission estimation model, the spatio-temporal evolution of carbon emission in Fujian from 1992 to 2022 was analyzed at city-county level. The results show that: (1) Temporal dimension: carbon emission in Fujian increased from 37.18 million tons in 1992 to 324.1 million tons in 2022. (2) Spatial dimension: carbon emissions in Fujian showed "high in the east and low in the west" and "high in the south and low in the north". The global spatial distribution is clustering spatial distribution. L-L clustering is mostly in the northern part of Fujian, but not significant in the middle part, and H-H clustering is mainly in the southeast coast. The results of this study can provide data support for promoting the comprehensive green transformation of economic and social development.

Ultra-wideband (UWB) positioning technology is prone to be obscured by various objects in indoor complex environments and generates non-line-of-sight errors (NLOS). In view of the current situation that there are few studies on how to deploy UWB in indoor complex environments to ensure positioning accuracy while minimizing the use of UWB base stations and saving costs, this study generates positioning ranges based on AGV tracking routes; divides positioning ranges based on UWB positioning ranges to generate basic UWB deployment points; analyzes blind areas of UWB signals through threedimensional spatial analysis technology; encrypts and deploys UWB base stations for blind areas; and analyzes the rationality of dense deployment points through three-dimensional spatial analysis technology. Finally, through simulation experiment, a scientific and reasonable base station deployment process is provided for UWB deployment in indoor complex environment.

To better understand and accurately determine the phenotypic parameters of scrub sands, a three-dimensional reconstruction methodology has been proposed that relies on ground-based LiDAR and voxel modeling. The first stage involves using FaroFocus to gather point cloud data of the scrub from various angles, optimizing the collection process through information fusion. Next, the point cloud data is refined through background removal and radius filtering, setting a radius threshold to isolate a single scrub sand mound point cloud. To enhance computational efficiency, the point cloud volume is managed through down-sampling and fine denoising procedures. Lastly, the voxel method is employed for 3D reconstruction. Key features such as plant height, crown width, surface area, and volume of the scrub sand mound are computed using the alpha-shape algorithm and the convex hull method. These methodologies are utilized to align the single scrub sand mound, thereby achieving a comprehensive reconstruction of the scrub sand mound model. The research findings indicate that the introduction of down-sampling and fine denoising processes significantly improves the efficiency of point cloud reconstruction. The resulting model is highly accurate and realistic, closely resembling the actual scrub sand mound. This reconstructed model offers a visual research foundation and technical support for the study of scrub sandpile characteristics.

Condition observation and indirect observations are the two basic models for data processing measurement adjustment. This paper applies the transformation of conditional adjustment and indirect adjustment function models to calculate the annexed route and closed error for the GPS network. We can find the relationship between the conditional equation and the coefficient matrix of the error equation, and device the information of annexed route or closed loop in GPS control network by matrix transformation, so that we can obtain the error equation directly and calculate the corresponding closure error. In this paper, we use the program and process the data of the example to verify the method can search the annexed route and the closed loop, and the closing error of GPS control network can satisfy the accuracy requirement.

In this paper, we propose a methodology for extracting rice planting areas using high-resolution remote sensing images, while also considering vegetation phenology. This method utilizes the typical spectral and texture features of rice during the early planting, filling, and post-harvest phases. Combined with vegetation index and texture variance methods, this approach enables the assessment of the favorable attributes of rice during the early planting phase, as well as the distinctive characteristics of rice during the filling and post-harvest phases. Our objective is to integrate remote sensing extraction findings with management data to improve recognition accuracy within the spatial constraints of natural resource surveys and monitoring. The accuracy of extracting rice planting areas has been determined to be 95.1% through field operation verification.

Taking Yuehai Lake Wetland in Yinchuan City, Ningxia as the research object, based on Landsat-8 image data, combined with the detection values of water quality parameters taken in the field, a remote sensing water quality parameter inversion model was established, and the concentrations of four water quality parameters of chemical oxygen demand, ammonia nitrogen, total phosphorus and total nitrogen were inverted, and the concentration of water quality parameters was comprehensively analyzed and evaluated by entropy weight fuzzy method. The results show that the established inversion model can reflect the parameter concentration distribution of Yuehai Lake water quality in different areas. The fitting degree R² of the four water quality index inversion models of chemical oxygen demand, ammonia nitrogen, total phosphorus and total nitrogen were 0.88, 0.89, 0.87 and 0.9, respectively; spatially, the pollution of the parameters in the north of Yuehai Lake is more serious than that in the south. The comprehensive evaluation results of water quality showed that the water quality of Yuehai Lake was mainly Class II and Class III water quality.

The "planned" supply of tobacco lags behind the dynamic development of the city, making the mismatch between the supply and demand of tobacco products even more serious. Based on the POI data of the urban living service industry and tobacco product retailer, the spatial kernel density estimation and standard deviation ellipse analysis tools were used to construct a new spatial layout suitability analysis model of tobacco product supply and demand. The analysis of Guanshanhu District in Guiyang City using the proposed model in this study shows that the overall supply of tobacco products in Guanshanhu District is adequate, the spatial distribution of tobacco product retail points and living facilities in Guanshanhu District is consistent, and the spatial distribution of supply is generally reasonable; however, from the perspective of population distribution and geographic space, the spatial layout of tobacco product supply and demand in Guanshanhu District still has more space for development. The analysis results are consistent with the facts in Guanshanhu, indicating that the proposed model can provide a reference for the spatial suitability analysis of tobacco product supply and demand in other regions.

This study aims to investigate the wetland vegetation phenology in the Yellow River Delta using remote sensing data from the Sentinel-2 satellite and four classification algorithms: Support Vector Machine (SVM), Classification And Regression Tree (CART), Gradient Tree Boost (GTB), and Random Forest (RF). By constructing feature sets and analyzing time series curves, the phenological information of wetland vegetation, including Spartina alterniflora (SA), Suaeda salsa (SS), Phragmites australis (PA), and Willow (WW), was explored and the classification accuracy of different algorithms was compared. The results demonstrate distinct phenological characteristics of wetland vegetation in the Yellow River Delta, with RF algorithm showing excellent performance in accurately extracting large areas of SA and achieving good results in mixed vegetation areas. The invasion of SA poses a significant threat to native vegetation, gradually occupying their growth space. This study provides scientific decision support for the ecological restoration and conservation of wetland vegetation in the Yellow River Delta.

Structural health monitoring (SHM) is crucial in preserving the civil infrastructure asset and ensuring safety of the operations. Amongst the available SHM techniques, the ground-based synthetic aperture radar (GB-SAR) is one of the most reliable. However, a gap in knowledge with the use of this system exists when multiple targets are in the same acquisition range. The present study investigates into this aspect and proposes a two-stage procedure based on i) controlling the signal propagation characteristics during the data collection and ii) implementing advanced signal processing techniques to aid the interpretation of the measured signal. To this effect, three scenarios of interest are implemented in the laboratory environment, i.e., i) absence of targets, ii) presence of one target, and iii) presence of two targets in the centerline of the radar. The data collection is aided by augmented reality (AR), which allows to visualise the radar footprint and precisely control the acquisition according to the set scenarios. The collected data are processed using the empirical mode decomposition (EMD) and the Hilbert-Huang transform (HHT) techniques. The proposed methodology is shown to be effective in both the data control and processing stages. Results have proven that the signal response from multiple targets differs from that observed in the other investigated scenarios, hence showing potential for enhancing multi-target detection in structures with GB-SAR.

Scenic spots are important ecological conservation targets in China, and remote sensing monitoring is a crucial technique used for various regulatory purposes. In this study, utilizing two phases of Chinese high-resolution satellite imagery from 2021 to 2022, remote sensing technique is employed along with interactive interpretation methods to establish interpretation indicators for natural and anthropogenic factors within scenic spots in Zhejiang Province. Based on the change detection between the two temporal images, this study identifies that 17 out of the 22 national-level scenic spots experienced changes in natural resources. A total of 501 patches related to natural resource alterations are detected, including 463 patches indicating degradation and other 38 ones indicating restoration. This demonstrates that remote sensing can significantly enhance the efficiency of regulatory for management authorities and effectively address the limitations of traditional monitoring approaches. It can provide objective and authentic data for dynamic monitoring and investigation of natural resources and ecological geological environments within scenic spots in the study area, offering fundamental information and technical support for real-time monitoring of development and construction activities conducted by relevant sectors.

In recent years, the stability of ecosystems in the Yellow River Delta has been damaged by the influence of invasive alien species such as Spartina alterniflora, so monitoring and protecting the wetland environment is of great significance. In this study, based on the Google Earth Engine platform, multiple features of Landsat optical satellite remote sensing data were assembled: vegetation index, water index, texture features, and land surface temperature features. The feature dataset was optimized, and finally, the wetland information was extracted from the optimized feature variables using the Support Vector Machine classification method. In this paper, the estuary of the Yellow River Delta is taken as the study area, and the wetland classification results at the estuary of the Yellow River Delta from 1990 to 2020 were obtained. The results show that (1) the surface temperature feature is of great significance for the classification of wetlands in the Yellow River Delta, and it ranks fourth in the order of importance. (2) The number of composite images has less influence on the final results. In 1990, four images were used to synthesize, and the overall accuracy and Kappa coefficient of classification results reached 89.15% and 0.832. In 2002, 12 images were used to synthesize, and the overall accuracy and Kappa coefficient of classification results reached 87.95% and 0.822. The research results in this paper can provide a basis for decision-making on the restoration and protection of wetlands in the Yellow River Delta.

To solve the research requirement that optical data is difficult to meet the temporal classification, the advantages of radar data images are brought into play to accurately and effectively count the winter wheat planting information. Taking dualpolarized Sentinel-1A SAR as the data source, a spatial distribution extraction method of winter wheat with joint SAR backscattering features and Polarization decomposition features was constructed. Support vector machine, random forest and artificial neural network classifier were used to obtain the extraction result map and accuracy of winter wheat in the study area. The differences in the extracted winter wheat planted area using the joint feature SAR datasets of different growing periods of winter wheat and the relative errors with the official statistics were compared. The results show as follows: 1) The accuracy of winter wheat extraction from time-series SAR joint feature data is better using RF classifier, and the accuracy of producer and user reaches 95.25% and 93.29%, respectively. 2) By comparing the time series SAR data of different growth periods to extract winter wheat results, it is found that the extraction result of winter wheat using the SAR dataset at tassel stage is the closest to those of the complete growth period, which provides basic data for developing winter wheat planting area and estimating yield before immature winter wheat.

Satellite-derived ocean color data are critical and useful for long-term monitoring and understanding of ocean biological and ecological dynamics and phenomena. However, the large-scale missing data limits their applications in studying physical and biological ocean processes monitoring. In the study, we took an improved data interpolating empirical orthogonal functions (VE-DINEOF, variable EOFs DINEOF) to reconstruct the daily 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 ocean color products of Bohai Bay to analyze the local oceanic characteristics and to validate the VE-DINEOF method for MODIS Level-2 ocean products. The results show that the VE-DINEOF method can effectively gap-fill the missing data and compared with the original DINEOF method, the VE-DINEOF method can enhance the reconstruction accuracy for various ocean color products. In addition, the seasonal distribution of chlorophyll concentration shows that the Haihe River is a significant source for chlorophyll concentration in Bohai Bay. Moreover, some details about the VE-DINEOF method were also discussed in this paper to explain the advantages of the proposed method.

Bamboo forests, especially Moso bamboo forests, are vital forestry resources due to their rapid growth rate and significant capacity for carbon sequestration. Accurate assessment of the quantity and spatial distribution of bamboo forest resources is essential for the development of the bamboo industry and the assessment of its ecosystem services. However, remote sensing techniques face limitations in detecting Moso bamboo forests due to spectral similarity with other vegetation and the presence of cloud cover. In this study, we used random forests through the Google Earth Engine (GEE) platform in combination with spectral indices, texture and topographic features to classify the study area in Wuxing District, China. The results show that: it is not that the more features the higher the classification accuracy, after the selection of features, the number of features was reduced from 40 to 28, and the classification accuracy (overall accuracy 92.69%, Kappa coefficient 0.91) was slightly improved compared with that before the optimization (overall accuracy 92.17%, Kappa coefficient 0.90). In addition, Topographic features play an important role in land cover classification. This study provides a reference for extracting spatial features using open source remote sensing data.

Arc array bistatic SAR with a satellite transmitter is a new imaging mode of array radar, which has a good application prospect in helicopter emergency rescue, military, environmental monitoring and other fields. However, due to the special structure of the receiving antenna, obtaining the two-dimensional spectrum directly from the fixed-phase principle poses a considerable challenge. In addition, when the satellite is moving at high speed, there is a serious range migration. To address the aforementioned challenges, this paper proposes an innovative imaging algorithm based on series inversion. Firstly, the range migration introduced by the satellite's high-speed motion is compensated in the range frequency domain. Then, the two-dimensional spectrum of echo data is obtained by the method of series inversion, the corresponding compensation factors are constructed to complete the migration correction and secondary range compression. Finally, simulation test is used to validate the suggested algorithm's efficacy.

The Xiangjiang River is the largest river in Hunan, and its water quality is crucial to Hunan's social and economic development. Although some scholars have conducted water quality assessments on the Xiangjiang River, these water quality evaluations are based on a certain section or time point, and do not analyze the Xiangjiang River Basin from the perspective of the overall situation and long-term period. To this end, by selecting the water quality monitoring data of 8 water quality monitoring stations in Xiangjiang River from 2021 to 2022, the data set was constructed. The water quality evaluation method was carried out by using the single-factor evaluation method. The Mann-Kendall trend test method and cluster analysis method, combined with GIS and DEM, were used to analyze the water quality changes of the entire Xiangjiang River in 2021-2022 from the perspective of time and space. The test results show that the overall water environment quality of the Xiangjiang River Basin is excellent, and the annual and quarterly water quality evaluation results are above Class III. The concentration of NH3N and TP in spring and summer are generally higher than that in autumn and winter. After hierarchical cluster analysis, it can be seen that the water quality of April to August in the Xiangjiang River Basin is worse than that of other months, and the water quality of Zhangshugang and Xingang stations is worse than that of other stations.

This article utilizes MICAPS observation data and Qingdao coastal automatic station and island station observation data, uses the dual channel difference method (DCD) based on the data of Japan's MTSAT geostationary meteorological satellite, and uses the method of identifying and path tracking of the Jianghuai cyclones, to extract the sea fog events in Qingdao that caused by Jianghuai cyclones. Using this identification method, a study is conducted to the relationship between the Jianghuai cyclones and the development of sea fog events in Qingdao from March to July 2011to 2018. It is found that 24% of the Jianghuai cyclones cause heavy fog events in Qingdao due to the warm and humid air flow in front of the cyclones. 56.2% originate in the east of 116°E, and the location of entering the sea is concentrated in 30°N～34°N. When there is a deep and wide westerly trough and a strong high-pressure ridge from the Korean Peninsula to the sea of Japan, the Jianghuai cyclone moves northeastward, the southeast air flow in front of the cyclone continues, accompanied by a strong warm advection in the lower layer, if the relative humidity, temperature inversion, vertical upward velocity, air sea temperature difference and sea water temperature gradient in the offshore of Qingdao reach the prediction indicators, then sea fog event appears in Qingdao.

A weighted fusion location method based on multilevel fault-tolerant mechanism is proposed to solve the problems of range relay test, real-time outlier elimination and false target tracking by a few equipment. Through the application of multilevel fault-tolerant algorithm, the target position can be calculated normally without identifying and eliminating the outliers of the measured data and the false targets of a few devices in the preprocessing stage. The weighted fusion positioning method is used to further improve the measurement accuracy. The simulation results show that the proposed method has a good fault-tolerance capability for the measured data, which can not only make full use of the measured data of photoelectric theodolite, but also perform positioning calculation and false target identification without outage elimination, so as to ensure the reliability of positioning results. In the process of changing target position and data source, the measurement data can be automatically selected for fusion processing, and the trajectory switching is smooth, which meets the requirements of relay measurement. At present, the proposed algorithm has been applied in a real-time fusion processing system of a shooting range, and has realized the accurate guidance of the photoelectric theodolite station that failed to capture and track in multiple measurement tasks

UAV navigation and positioning research is critical to flight safety, complex environment response and aviation technology advancement. Global research has focused on multi-sensor fusion and visual navigation, but challenges such as positioning accuracy, multi-modal fusion and real-time performance still need to be addressed. Continued in-depth research will improve UAV positioning accuracy, environment awareness and autonomous navigation capabilities, and promote the expansion of UAV technology applications. This paper focuses on the advantages of radar and unmanned aerial vehicles (UAVs), such as strong concealment and anti-jamming, which are one of the key equipments in modern military operations. The existing passive positioning methods have the defect of large target positioning error, which cannot meet the needs of today's warfare. Therefore, this study proposes a UAV positioning and formation analysis based on passive dual-station positioning and optimised models. The results show that the model proposed in this study has good robustness.