Dataset "Authomatic landslide detection using deep learning approach"
Authors/Creators
- 1. Department of Forest Sciences, Faculty of Agriculture and Forest Sciences, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco, Chile, 4780000
- 2. Department of Computer Architecture and Technology, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Spain
- 3. Facultad de Ingenieria Agricola, Universidad de Concepcion, Chillan 3812120, Chile
- 4. Key Laboratory of Mountain Hazards and Earth Surface Processes, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Description
Below, compressed in a rar file, we present the data set developed during the research "Authomatic landslides detection using deep learning approach", submitted to the journal Landslides.
“Landslide_database” folder: contains two ESRI shapefile-type vector files, the first (Ground_Truth_database), corresponds to the manually outlined landslides for training the deep learning model. The file contains the following fields: associated Sentinel 2 image for the bounding (S2), area, and perimeter. The second (Ground_Truth_database_centroid) corresponds to the centroid of the outlined landslides, in addition to the previously mentioned fields, it contains the X and Y coordinates.
“Model results” folder: contains an ESRI shapefile vector file (Pred_T18GYS), corresponding to the landslides detected and segmented by the deep learning algorithm in the Sentinel-2 test tile.
“Model validation” folder: contains multiple ESRI shapefile vector files used during model validation. Study area (Study_Area), roads (Roads) and populated areas (Localities). Contains the predicted landslides in the study area (Predict_T18GYS_SA), the randomly selected predicted landslides (Predict_T18GYS_Random) and their geometries (Predict_T18GYS_Geometry). The folder also contains manually outlined landslides (Groud_Truth_T18GYS) restricted to the extent of the evaluated tile, and outlined landslides that spatially match randomly selected predicted landslides (Ground_Truth_Random). Finally, the true positives (TP_T18GYS), false positives (FP_T18GYS) and false negatives (FN_T18GYS) are provided separately.