MBANet: Multiscale Boundary Aware Network for Landslide Identification on Remote Sensing Imagery

Abstract

Semantic segmentation of remote sensing imagery has been widely applied in landslide identification, effectively addressing the time-consuming and labor-intensive nature of manual visual interpretation. However, existing models still face challenges in extracting multiscale features and accurately delineating boundaries under complex background conditions. To overcome these limitations, this study proposes a multiscale boundary aware network (MBANet) for landslide identification in remote sensing imagery. Specifically, we design a multiscale cross-interaction convolution (MCC) module that captures local details and broader contextual cues through heterogeneous receptive-field branches, and recalibrates the concatenated multiscale features via an adaptive cross-branch interaction strategy. In addition, a boundary sensitive refinement attention (BSRA) module is introduced to enhance boundary localization by combining a Sobel-based gradient prior, learnable boundary estimation, and region-context enhancement for fine-grained boundary refinement. These modules are integrated into an encoder–decoder architecture to jointly achieve semantic consistency and boundary precision. Experimental results on two public datasets show that MBANet outperforms other comparison models in overall segmentation performance and maintains competitive performance in boundary delineation. On the Bijie dataset, it achieves a recall of 83.84% and an $F1$ -score of 85.39%; on the Palu dataset, it reaches a recall of 75.83% and an $F1$ -score of 78.17%, highlighting its superior performance.