Landslide Instance Segmentation from Remote Sensing Imagery Using a SwinV2–FPN Mask R-CNN with Spatially Adaptive Feature Modulation

Landslide inventory mapping supports emergency response and hazard assessment in mountainous regions. Accurate identification and delineation of individual landslides remain challenging because of scale variation, irregular morphology, and interference from visually similar backgrounds in complex mountainous terrain. To address these challenges, we modified Mask R-CNN’s feature pathway and mask supervision. ResNet50 was replaced with SwinV2-Tiny for hierarchical feature extraction, while the feature pyramid network (FPN) was retained for multi-scale fusion. Spatially adaptive feature modulation (SAFM) was applied after the FPN to adjust spatial responses using local features and broader context. Binary cross-entropy (BCE) and Dice losses were combined to supervise pixel-wise foreground–background discrimination and instance-mask overlap. A curated dataset combining imagery from Bijie, the Diverse Mountainous Landslide Dataset (DMLD), and ScienceDB was used for training, validation, and internal testing. All configurations were evaluated using the Common Objects in Context (COCO) instance-mask protocol. Across three training seeds, SwinV2–FPN–SAFM Mask R-CNN achieved an internal test average precision (AP0.50:0.95) of 0.6158±0.0082 (mean ± sample standard deviation). Its mean mask AP0.50:0.95 exceeded those of ResNet50–FPN Mask R-CNN and Mask2Former–Swin-T by 6.21 and 3.51 percentage points, respectively. It achieved the highest mean mask AP0.50:0.95 among the ten evaluated configurations. The ablations indicated small positive increments in mean mask AP0.50:0.95 with SAFM and BCE–Dice, and their incremental benefits remained uncertain. The higher mean mask AP0.50:0.95 indicates improved overall performance in identifying landslide instances and delineating their extent on the internal test set. The resulting instance masks may assist inventory compilation and post-disaster mapping in complex mountainous terrain.