Automatic recognition of landslides based on YOLOv7 and attention mechanism

Landslide disasters comprise the majority of geological incidents on slopes, posing severe threats to the safety of human lives and property while exerting a significant impact on the geological environment. The rapid identification of landslides is important for disaster prevention and control; however, currently, landslide identification relies mainly on the manual interpretation of remote sensing images. Manual interpretation and feature recognition methods are time-consuming, labor-intensive, and challenging when confronted with complex scenarios. Consequently, automatic landslide recognition has emerged as a pivotal avenue for future development. In this study, a dataset comprising 2000 landslide images was constructed using open-source remote sensing images and datasets. The YOLOv7 model was enhanced using data augmentation algorithms and attention mechanisms. Three optimization models were formulated to realize automatic landslide recognition. The findings demonstrate the commendable performance of the optimized model in automatic landslide recognition, achieving a peak accuracy of 95.92%. Subsequently, the optimized model was applied to regional landslide identification, co-seismic landslide identification, and landslide recognition at various scales, all of which showed robust recognition capabilities. Nevertheless, the model exhibits limitations in detecting small targets, indicating areas for refining the deep-learning algorithms. The results of this research offer valuable technical support for the swift identification, prevention, and mitigation of landslide disasters.