Abstract: 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.