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While deep learning approaches have shown promise for semantic segmentation, existing models face limitations in extraction accuracy and processing efficiency for landslide detection applications. We propose the MED-DeepLabv3+\u00a0model to resolve these challenges. The proposed model incorporates three key improvements: (1) Replacing Xception in the encoder with the MobileNetV3 feature extraction network enhances the model\u2019s focus on landslide semantic features and reduces the size of the model parameters; (2) Integrating an Efficient Multi-scale Attention module to refine shallow feature representations and enhance multi-scale feature extraction; (3) Introducing a DS-ASPP module, which replaces standard convolutions with Depthwise Separable Convolutions and incorporates Squeeze-and-Excitation and Strip Pooling modules to improve deep feature recognition. Additionally, the singularity of publicly available landslide datasets poses a significant challenge for training deep learning models in this domain. To mitigate this issue, we construct a diverse deep learning-based landslide dataset tailored for landslide recognition research. Results demonstrate that MED-DeepLabv3\u00a0+\u00a0achieves superior performance in landslide detection, obtaining a Mean Intersection over Union (MIoU) of 81.54%, pixel accuracy of 94.63%, precision of 77.69%, recall of 86.35% and an F1-score of 81.79%. Compared to the baseline DeepLabv3\u00a0+\u00a0model, MED-DeepLabv3+\u00a0achieves higher detection accuracy while maintaining a lightweight architecture, making it well-suited for rapid and precise landslide identification.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf108","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T12:08:33Z","timestamp":1760530113000},"page":"1-23","source":"Crossref","is-referenced-by-count":0,"title":["MED-DeepLabv3+: a lightweight landslide recognition algorithm on multi-scale remote sensing images"],"prefix":"10.1093","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8721-1001","authenticated-orcid":false,"given":"Xuhui","family":"Li","sequence":"first","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University , 88 Anning West Road, Anning District, Lanzhou, Gansu 730070 ,","place":["China"]},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National 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