Landslide identification based on an improved YOLOv7 model: A case study of the Baige area
LIU Haoran1,2(), YAN Tianxiao1,2, ZHU Yueqin2(), WANG Yanping1, CHEN Zuyi1, YANG Zhaoying3, ZHU Haomeng4
1. Institute of Disaster Prevention Science and Technology, Langfang 065201, China 2. National Institute of Natural Disaster Prevention, Ministry of Emergency Management of China, Beijing 100085, China 3. China Aero Geophysical Survey and Remote Sensing Center, Beijing 100083, China 4. Zhejiang Institute of Geosciences, Hangzhou 310000, China
Landslide identification has always been a research topic in the study of geological disasters, playing a significant role in emergency rescue and command. To address the limitations in landslide identification, such as missed/false detection, and low identification accuracy, this study proposed an improved YOLOv7 model that enables simultaneous object detection and image segmentation for landslides. The improved model optimized its core network by integrating data, adding the convolutional block attention module (CBAM), and changing the intersection over union (IoU) loss function. Its effectiveness was verified using the landslide dataset of Bijie City, Guizhou Province, and the 0.2 m high-resolution digital orthophoto map (DOM) of historical landslides in Sichuan Province. The results indicate that the improved model performed well in landslide detection and segmentation, achieving more efficient and accurate landslide identification compared to the conventional YOLOv7 model, and other prevailing models like Fast RCNN and Mask RCNN. Taking the Baige area in Sichuan Province as an example, this model can effectively enhance the automation level of landslide disaster information acquisition while improving accuracy.
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LIU Haoran, YAN Tianxiao, ZHU Yueqin, WANG Yanping, CHEN Zuyi, YANG Zhaoying, ZHU Haomeng. Landslide identification based on an improved YOLOv7 model: A case study of the Baige area. Remote Sensing for Natural Resources, 2025, 37(4): 48-57.
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