Method of determining urban sites prone to waterlogging based on remote sensing big data

Abstract

The invention discloses a method of determining urban sites prone to waterlogging based on remote sensing big data. A geographic information system technology is adopted to obtain urban DEM data with urban remote sensing image data; the massive DEM data is restored in a Hadoop Distributed File System (HDFS) of Hadoop; the method of determining the urban sites prone to waterlogging is brought up based on a big data processing technology, parallel processing of the method of determining the urban sites prone to waterlogging is achieved through Map-Reduce, and the sites prone to waterlogging are determined quickly; a WebGIS technology is adopted to visually display the spatial positions of the urban sites prone to waterlogging obtained through calculation on an electronic map. Compared with a traditional method, the method of determining the urban sites prone to waterlogging based on the remote sensing big data can effectively and fast find the urban sites prone to waterlogging and visually display the spatial positions of the urban sites prone to waterlogging, and has high social and economic benefits and a wide application prospect range in the pre-warning field of urban waterlogging disaster.

Description

technical field [0001] The invention belongs to the technical field of big data analysis, and more specifically relates to a method for determining urban waterlogging-prone points based on remote sensing big data. Background technique [0002] With the acceleration of urbanization and the impact of climate change, urban waterlogging disasters have occurred frequently in recent years, which has become a severe challenge for urban development in my country. In order to reduce the severe losses caused by urban flood disasters, all waterlogging-prone points in the city should be identified first, and key monitoring and treatment of waterlogging-prone points should be carried out. The existing method of finding waterlogging spots is mainly manual field survey, which not only consumes a lot of manpower and material resources, but also has low efficiency, and it is impossible to quickly track the changes of waterlogging spots caused by urban construction, and it is also impossible ...

AI Technical Summary

Problems solved by technology

The existing method of finding waterlogging spots is mainly manual field survey, which not only consumes a lot of manpower and material resources, but also has low efficiency, and it is impossible to quickly track the changes of waterlogging spots caused by urban construction, and it is also impossible to intuitively obtain the space of waterlogging spots Therefore, it is difficult to quickly release early warning information of water damage points, causing troubles and losses to urban residents' traffic travel and their lives and properties

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