Treffer: Cross-Regional Detection and Precise GIS Localization of Old Landslides Using High-Resolution Remote Sensing Imagery and YOLOv5.

Highlights: What are the main findings? A YOLOv5-based framework enables effective cross-regional detection of old landslides using high-resolution remote sensing imagery. A Python–GIS post-processing strategy converts detection outputs into accurately georeferenced landslide shapefiles. What is the implication of the main finding? The proposed approach significantly improves the efficiency and spatial accuracy of large-area old landslide inventories. The framework provides a practical and transferable solution for landslide hazard investigation and risk management. Old landslide reactivation poses a significant risk to infrastructure and settlements in mountainous regions. Its identification and accurate localization are crucial for mitigating reactivation hazards, yet are hindered by blurred morphological signatures and vegetation cover. This study develops a cross-regional workflow for the detection and GIS-based localization of old landslides using one-meter-resolution optical imagery and an enhanced YOLOv5 model. The workflow strictly separates training and detecting areas (Wanzhou for training, Zigui for detecting) to simulate realistic, unsurveyed scenarios. A Python script converts model outputs into shapefiles with precise geographic coordinates. The results show an F1 score of 0.96 in the training area and 0.62 (mAP@0.5 = 0.58, Precision = 0.56, Recall = 0.67) in the detecting area. The analysis identifies causes of cross-regional performance degradation, including geomorphic confusion and potential detection of previously unmapped old landslides. These results demonstrate the feasibility of cross-regional landslide detection and highlight the potential of deep learning–GIS integration for practical hazard management. [ABSTRACT FROM AUTHOR]

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