The health condition of road pavement has a crucial impact on driving safety. Traditional pavement detection methods have various shortcomings, so this paper proposes a road crack detection method based on UAV images. The target area is segmented and extracted after acquiring the road image by applying DJI Jingwei M210-RTK UAV aerial photography. Through the GUI platform of MATLAB, the image preprocessing, crack recognition and classification operations are performed on the image to determine the type of cracks and mark them. The length, area, average width and other parameters of the cracks were calculated by pixel scanning. It is proved that this detection method can efficiently and accurately realize the functions of pavement crack recognition, type judgment and parameter information extraction.
Urban water network landscape and hydrological stability are directly related to the healthy development of urban landscape patterns and ecosystems. In this paper, major rivers within the fourth Ring road of Shenyang City are selected as the research object. Based on the high-resolution remote sensing image data of Landsat8 in 2013 and Sentinel-2 in 2021 and the elevation data of DEM in 2021, the landscape and hydrological changes of the water network in Shenyang City are analyzed with the technical support of geographic information system (GIS). The results show : the landscape structure of the Shenyang urban water network is not reasonable, and the water network has low linear connection characteristics and ring connection characteristics. The actual hydrological data of Shenyang changed greatly compared with the hydrological extraction results of DEM: a large number of natural river channels in the lower reaches of Hunhe River were artificially reconstructed, Puhe River received natural reconstruction many times, and the number of artificial ditches in Tiexi District was quite large. Based on the above analysis, combined with the current situation of the Shenyang water network, this paper puts forward reasonable optimization suggestions and corresponding protection and management remedies, to provide guarantee for the sustainable development of Shenyang city, in order provide reference for the planning and construction of water network in other cities.
For the evaluation of traffic infrastructure, asphalt pavement aging conditions are crucial. Due to the complexity of identifying and monitoring asphalt pavement aging conditions, many current studies tend to use satellite remote sensing methods. We conducted an extraction experiment on the aging status of asphalt pavement using on-site measured Pavement Surface Condition Index data and Gaofen-2 satellite (GF-2) high-resolution remote sensing images based on comprehensive references to previous research results. Based on our experimental results, the difference health index, ratio health index, and normalized difference health index can reflect asphalt pavement aging to varying degrees, but the correlation is relatively weak. The purpose of this paper is to propose a new asphalt pavement aging index (PAI), namely the PAI, based on sufficient experimental analysis. In addition to identifying asphalt pavement aging perfectly, PAI has a good ability to discriminate between road interference information, such as shadows and vehicles, after it has been verified. There is a significant linear relationship between its correlation coefficient R and pavement surface condition index, which is 0.894. The evaluation results of three sets of ground verification points obtained by applying PAI also demonstrate its practicality. Therefore, the combination of PAI and GF-2 high-resolution remote sensing images can be used to evaluate the aging status of asphalt pavements.
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