A Spike-Inspired Adaptive Spatial Suppression Framework for Large-Scale Landslide Extraction

Landslides endanger human safety and damage infrastructure, underscoring the importance of accurate extraction. However, landslide extraction is often hindered by the omission of sparsely distributed landslides and the difficulty of delineating their blurred boundaries. Large-scale landslide extraction faces two key challenges. The first is a severe sample imbalance between landslides and background objects, which biases the model toward background and omits landslides. The second is the confusion between landslides and background features, which leads to inaccurate boundary delineation and fragmented extraction results. To address these issues, this paper proposes a two-phase landslide extraction framework. First, we propose a PCA-based landslide candidate extraction module to remove salient background objects and reduce data imbalance. Second, we propose a Spike-inspired Landslide Extraction Model to further discriminate actual landslides from the candidates by incorporating a spike-inspired sparse attention module (SISA). It can enhance weak landslide features such as blurred boundaries while mitigating background noise through its adaptive spatial suppression mechanism. To integrate spatial details across scales, a mix-scale feature aggregation module (MSFA) is proposed, which aggregates hierarchical features to extract landslides of various scales. Experiments on the landslide datasets from the Hengduan Mountains and Hokkaido, Japan, show IoU improvements of 4.26% and 1.22% compared to the recently proposed methods, validating its effectiveness under both imbalanced and dense landslide conditions.