Detection of Sinkholes and Landslides in a Semi-Arid Environment Using Deep-Learning Methods, UAV images, and Topographical Derivatives

Abstract

Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes respectively, both often triggered by intensive rainfall. These processes often cause problems in the hilly regions in the “Golestan province” of Iran, and their detection is the essential aim for this research. The production of soil landforms maps is typically based on visual interpretation of aerial and satellite images eventually supported by field surveys. Recent advances in the acquisition of images from “unmanned aerial vehicles (UAV)” and of “deep learning (DL)” methods inherited from computer vision have made it feasible to propose semi-automated soil landforms detection methodologies for large areas at an unprecedented spatial resolution. In this study, we evaluate the potential of two cutting-edge DL segmentation models, the vanilla “U-Net model” and the “Attention Deep Supervision Multi-Scale U-Net” model, applied to “UAV”-derived products, to map landslides and sinkholes in a semi-arid environment, the “Golestan Province” (north-east Iran) Results show that our framework can successfully map landslides in a challenging environment (with an F1-score of 69%), and that topographical derivates from “UAV-derived DSM” decrease the capacity of mapping sinkholes of the models calibrated with optical data.

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

“…Using supervised learning, they built a model to predict potential sinkhole formations based on anomalies in the data. With an accuracy rate of over 90%, this tool became instrumental for urban planners and property developers in avoiding areas at risk and planning remedial measures for existing structures [41].…”