LMAF-Net: Lightweight Multi-Attention Fusion Landslide Identification Network

Abstract

Landslides, as sudden, destructive geological events, threaten human lives, property, and infrastructure. Remote sensing and UAV imagery provide essential data for landslide identification, yet landslide targets often appear as sparse small objects, and existing lightweight models still lack sufficient accuracy under small-sample and small-target conditions. To address this, we propose LMAF-Net, a lightweight multi-attention fusion identification network employing MobileNetV3 as encoder and integrating boundary-aware coordinate attention, serial dual attention, and efficient channel attention modules to enhance feature discrimination while maintaining efficiency. Experiments on four public landslide datasets show that LMAF-Net achieves an F1-score of 96.09% and IoU of 92.47% on STTCLD, with only 3.77M parameters and 1.07G FLOPs, outperforming compared models in both accuracy and efficiency. Ablation studies confirm that boundary-aware coordinate attention reduces omission error by 1.75%, and serial dual attention improves F1-score by 1.11%. Generalization tests reveal that the model maintains F1-scores between 94.95% and 97.63% across cross-region and cross-sensor scenarios, demonstrating strong cross-domain robustness. Robustness tests indicate that under degradation conditions such as radiometric perturbation, blurring, compression, and random occlusion, the average F1-score drops by only 2.05%, verifying resistance to image degradation. This work provides an effective lightweight solution for landslide identification under challenging small-sample and small-target conditions.