LD-DETR: A Multi-Scale Landslide Detection and Segmentation Network for Complex Geological Environments

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.