Detection of karst depressions in brazil using deep semantic segmentation

This research aims to investigate the use of semantic segmentation and Shuttle Radar Topography Mission (SRTM) data in detecting natural karst depressions developed on the carbonate rocks of the Neoproterozoic Bambui Group in Western Bahia, Brazil. The study area is a karst landscape containing depressions enclosed in limestone, many forming lakes. The methodology had the following steps: (a) visual interpretation of karst depressions from Sentinel-2 and OLI-Landsat 8 images; (b) generation of DEM-based sink depth plus nine morphometric attributes; (c) selection of 128x128-pixel samples for training (1600), validation (400), and testing (400) considering two channels (DEM and sink depth based on DEM) and eleven channels (the two previous ones and the morphometric attributes); and (d) semantic segmentation using U-Net architecture with EfficientNet-B7 backbone. The accuracy metrics were 98.26, 72.82, 79.50, 79.16, and 65.51 for OA, precision, recall, F-score, and IoU when considering SRTM plus morphometric attributes (11 channels).