Factors and machine learning model results in Meizhou, China
Description
This is the relevant data for the paper titled Multi source Remote Sensing Assessment of Soil Type Controlling Regional Landslide Susceptibility after Heavy Rainfall in Meizhou, Guangdong, China. This data mainly includes machine learning related factors and calculated results, as well as some results related to sbas, e-sbas, and d-insar. Below, I will introduce the relevant documents. SBASandESBAS:This is the result obtained through ENVI using Sentinel-2, SBAS, and E-SBAS regarding the Meizhou research area. demandresearchArea:This document includes the elevation data of the study area and the scope of the study area (shp file). factors:This file is a data file for training machine learning. Hillshade:This file is a mountain shadow file, mainly used as a base image during drawing to make the image look better. historylandslide:This document contains historical landslide data as labeled data for machine learning. Landslidesusceptibilityresults(202311):This file is the result of machine learning, which differs from the factors of annual data. It mainly uses NDVI and rainfall data from November 2023, while other data is the same. The file includes all the files output by the model, and the model uses new landslide points as label data. Landslidesusceptibilityresults(202406):This document is the result of machine learning, with different factors from the annual data. It mainly uses NDVI and rainfall data from June 2024, and the rest is the same as the previous document. Landslidesusceptibilityresults(historylandslide):This file is the output file of machine learning, using historical landslide points as label data. Landslidesusceptibilityresults(newlandslide):This is all the result files of machine learning obtained using new landslide points as labels. newhistoryandSentinel-2image:This document contains satellite imagery data from Sentinel-2 and utilizes the imagery data to obtain newly generated landslide point data for training purposes.