Landslide recognition knowledge base establishment method and device, electronic equipment and storage medium

Abstract

The invention relates to a landslide recognition knowledge base establishment method and device, electronic equipment and a storage medium. The method comprises the following steps: carrying out the analysis of an SAR image data set of each target region through employing an InSAR technology, and determining a time sequence deformation diagram and an accumulated deformation diagram of each target region; based on the slope map and the slope direction map of the target area, deformation points in the time sequence deformation map of the target area are analyzed by jointly applying a hotspot clustering technology and a spatial clustering analysis technology, and the landslide boundary of the target area is determined; and based on a preset interpretation mark, performing remote sensing visual interpretation on the optical image data set, the time sequence deformation diagram, the accumulated deformation quantity diagram and the landslide boundary of the target area, and determining landslide attribute information of the target area. According to the method, the influence of the slope direction and the slope on landslide recognition is considered, the accuracy of the obtained landslide attribute information is improved, the data precision of a landslide recognition knowledge base is further improved, and a good foundation is laid for subsequent AI automatic recognition.

Description

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AI Technical Summary

Benefits of technology

This technology helps identify landslides more accurately through images from an area where they are located on top or underground. It uses this data to improve understanding about them better than previous methods based only on their appearance alone.

Problems solved by technology

This patents describes how ground slide accidents are caused through human factors such as accident investigations, concealed dangers assessments, and monitoring systems that use satellites. Current methods have limitations including high costs associated with analyzers themselves, limited range resolution, poor depth perception capabilities, difficulty recognizing small changes near landforms, and lacked automated means for accurate analysis of these issues. Therefore, current methodologies require expensive equipment like digital cameras or sophistically trained experts to achieve their goals.

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