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• W4385671402 abstract "This chapter analyzes the area where vertical displacements caused by anthropogenic factors occur. Karst sinkholes are formed in the study area. In the study area, these sinkholes appear due to the anthropogenic factor and the closure of mining enterprises. Traditional methods of research of such phenomena are interferometric methods. They are more complicated in technique and understanding. They allow you to accurately determine the magnitude of the displacement. Due to the use of Google Earth Engine cloud technology in this work, this method had to be abandoned. A Random Forest machine learning algorithm was developed to detect local vertical displacements of the Earth’s surface. Binary classification was used. Its advantage over other algorithms is that there is no need to process Single Look Complex data–this is used traditionally, but not in the Google Earth Engine database. The algorithm works based on the classification of the earth's surface and will work where the landform displacements are filled with water. The result is a time series of images that show a local increase in the water surface, which corresponds to an increase in land failure. It can be concluded that the presented method displays relief displacement in the given territory and can be used in other territories with vertical relief displacement, which are filled with water. It should be noted that the land dips are filled with water in those cases where the groundwater level is higher than the dip level of the sinkhole. This method should be used in such cases. It allows you to watch exactly the changes in the water surface, which filled the gaps in the ground." @default.
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• W4385671402 date "2023-01-01" @default.
• W4385671402 modified "2023-10-16" @default.
• W4385671402 title "Method for Detecting Natural and Anthropogenic Changes That Filled with Water in Landscapes Using Radar Satellite Imagery" @default.
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• W4385671402 doi "https://doi.org/10.1007/978-3-031-35088-7_46" @default.
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