Automated detection of landslide events from multi-source remote sensing imagery: Performance evaluation and analysis of YOLO algorithms

Landslides are among the most dangerous and catastrophic natural hazards with countless concerns. In disaster rescue operations, fast and precise identification of landslides is necessary for timely and effective preventive actions. The landslide risk is anticipated to be reduced through their prediction, monitoring, and accurate detection using remote sensing technology. Moreover, deep learning algorithms have shown excellent improvement in various remote sensing applications. Recent scientific and intelligent technological innovations are needed to be applied to disaster management and assessment, particularly landslides. Therefore, this study aims to extract the landslide hazard information from multiple data sources, i.e., satellite and unmanned aerial vehicle (UAV) images, using a single staged object detection model, i.e., YOLOv5, YOLOv6, YOLOv7, and YOLOv8. The data from distinct platforms are utilized to infer the synergies between them. The results of each database are evaluated quantitatively using standard methods, i.e., precision, recall, f-score, and mean average precision, whereas visual analysis of results is conducted for qualitative assessment. Based on the experimental results, the highest f-score is represented by YOLOv7 (0.995) and YOLOv5 (0.921) for satellite and UAV-based data, respectively. The quantitative results are further compared with previous research work to exhibit the novelty and competence of the proposed research. Our work demonstrates the application and feasibility of the YOLO model in landslide information extraction for quick hazard recovery operations.