With the continuous development of computer vision technology, more and more landslide identification detection tasks have started to shift from manual visual interpretation to automatic computer identification, and automatic landslide detection methods based on remote sensing satellite images and deep learning have been gradually developed. However, most existing algorithms often have the problem of low precision and weak generalization in landslide detection. Based on the Google Earth Engine platform, this study selected landslide image data from 24 study areas in China and established the DN landslide sample dataset, which contains a total of 1440 landslide samples. The original YOLOv7 algorithm model was improved and optimized by applying the SE squeezed attention mechanism and VariFocal loss function to construct the SE-YOLOv7 model to realize the automatic detection of landslides in remote sensing images. The experimental results show that the mAP, Precision value, Recall value, and F1-Score of the improved SE-YOLOv7 model for landslide identification are 91.15%, 93.35%, 94.54%, and 93.94%, respectively. At the same time, through a field investigation and verification study in Qianyang County, Baoji City, Shaanxi Province, comparing the detection results of SE-YOLOv7, it is concluded that the improved SE-YOLOv7 can locate the landslide location more accurately, detect the landslide range more accurately, and have fewer missed detections. The research results show that the algorithm model has strong detection accuracy for many types of landslide image data, which provides a technical reference for future research on landslide detection based on remote sensing images.

Landslides are among the most destructive natural hazards. High-resolution remote sensing imagery provides an effective means for landslide detection and mapping, supporting geological hazard assessment and early warning. However, existing methods still face challenges in complex mountainous scenes with heterogeneous land cover and geological conditions. These challenges include irregular segmentation boundaries caused by diverse landslide shapes, small-scale detection difficulties due to background interference, and foreground–background class imbalance that hinders effective model optimization. To address these issues, we propose LD-DETR, a network for landslide detection and segmentation. An Atrous Spatial Pyramid Pooling (ASPP) module is integrated into the segmentation head to improve multi-scale feature representation, enabling better segmentation of landslides with varying spatial sizes. To alleviate class imbalance caused by the dominance of non-landslide pixels, a weighted Tversky Loss is introduced to improve the learning of hard samples. Low-Rank Adaptation (LoRA) is further combined with orientation-aware data augmentation to enable parameter-efficient adaptation, reducing the proportion of trainable parameters of LD-DETR to 41.07% while keeping the pre-trained backbone parameters frozen. Experiments on the Bijie and Luding Landslide Dataset (BLLD) demonstrate that LD-DETR is competitive with the baseline model and several YOLO-based approaches. The proposed method achieves an F1-score of 95.18% and a Recall of 95%, while the segmentation task reaches an mAP of 59.28%, slightly higher than the RF-DETR baseline. Furthermore, a limited-sample adaptation experiment on the Jiuzhai landslide dataset demonstrates the transferability of the proposed method across different geographic regions. These results indicate that the proposed framework provides competitive landslide detection and segmentation performance and offers an approach for fine-grained landslide mapping using remote sensing imagery.