Sinkhole detection via deep learning using DEM images

Abstract Sinkholes, commonly observed in karstic regions, are landforms with unpredictable formation time and location. These landforms are an important natural disaster that threaten human life and country economies. Sinkholes, observed as cover collapse or depressions in nature, may be easily identified with remote sensing methods (digital elevation models, satellite and aerial photographs, etc.). These methods reduce time and labor burdens before field investigations. In this study, the aim was to accurately identify sinkholes by training a deep learning method with hillshade images obtained from DEM data. In line with this aim, the study area was identified as the Karapınar region (Konya, Türkiye), where many sinkholes have formed in recent periods linked to climatological and anthropological factors. In this study, 5 different (nano, small, medium, large and x-large) sinkhole detection models were developed using YOLO v8. The developed models provided high success rates in detecting sinkholes with different diameters and depths.