ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture
Abstract Landslides, as a prevalent geological hazard, pose a severe threat to both the environment and human society. The rapid and accurate identification of landslide-prone areas is crucial for disaster response, risk assessment, and urban planning. This paper proposes a novel deep learning-based...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98230-6 |
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| author | Xuqing Ren Xu Wu Donghao Zhai Xiangpeng Wang Ningbo He Mehreen Tarif |
| author_facet | Xuqing Ren Xu Wu Donghao Zhai Xiangpeng Wang Ningbo He Mehreen Tarif |
| author_sort | Xuqing Ren |
| collection | DOAJ |
| description | Abstract Landslides, as a prevalent geological hazard, pose a severe threat to both the environment and human society. The rapid and accurate identification of landslide-prone areas is crucial for disaster response, risk assessment, and urban planning. This paper proposes a novel deep learning-based landslide detection method ResM-FusionNet, which leverages ResNet-50 as the backbone for feature extraction and integrates a multi-layer perceptron as the decoder to enhance segmentation accuracy. To address the challenges of complex terrain and boundary detail detection in landslide-prone regions, we introduce a novel loss function, RLoss, which integrates masking mechanisms, semantic weighting, and a Top-K pixel loss averaging strategy. Experimental results on remote sensing datasets demonstrate that ResM-FusionNet significantly outperforms existing models. Specifically, ResM-FusionNet achieves 94.33% accuracy, 85.73% F1-score, and a Kappa coefficient of 70.12%, surpassing other models (e.g., SegFormer, DeepLabv3, and UNet) by 4.4%, 7.7%, and 17.6% in accuracy, respectively. Moreover, ResM-FusionNet excels in boundary detection, achieving an IoU of 0.7545, precision of 85.61%, and recall of 83.92%. These findings indicate that the proposed method not only provides robust and accurate landslide detection but also enhances segmentation performance in complex terrains. |
| format | Article |
| id | doaj-art-2a0bb2df16d0474cbc760f809acb054c |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2a0bb2df16d0474cbc760f809acb054c2025-08-20T03:18:38ZengNature PortfolioScientific Reports2045-23222025-04-0115111910.1038/s41598-025-98230-6ResM-FusionNet for efficient landslide detection algorithm with a hybrid architectureXuqing Ren0Xu Wu1Donghao Zhai2Xiangpeng Wang3Ningbo He4Mehreen Tarif5College of Computers Science and Cyber Security, Chengdu University of TechnologyCollege of Computers Science and Cyber Security, Chengdu University of TechnologyCollege of Computers Science and Cyber Security, Chengdu University of TechnologyCollege of Geophysics, Chengdu University of TechnologyChina ANNENG Group Third Engineering BureauCollege of Computers Science and Cyber Security, Chengdu University of TechnologyAbstract Landslides, as a prevalent geological hazard, pose a severe threat to both the environment and human society. The rapid and accurate identification of landslide-prone areas is crucial for disaster response, risk assessment, and urban planning. This paper proposes a novel deep learning-based landslide detection method ResM-FusionNet, which leverages ResNet-50 as the backbone for feature extraction and integrates a multi-layer perceptron as the decoder to enhance segmentation accuracy. To address the challenges of complex terrain and boundary detail detection in landslide-prone regions, we introduce a novel loss function, RLoss, which integrates masking mechanisms, semantic weighting, and a Top-K pixel loss averaging strategy. Experimental results on remote sensing datasets demonstrate that ResM-FusionNet significantly outperforms existing models. Specifically, ResM-FusionNet achieves 94.33% accuracy, 85.73% F1-score, and a Kappa coefficient of 70.12%, surpassing other models (e.g., SegFormer, DeepLabv3, and UNet) by 4.4%, 7.7%, and 17.6% in accuracy, respectively. Moreover, ResM-FusionNet excels in boundary detection, achieving an IoU of 0.7545, precision of 85.61%, and recall of 83.92%. These findings indicate that the proposed method not only provides robust and accurate landslide detection but also enhances segmentation performance in complex terrains.https://doi.org/10.1038/s41598-025-98230-6Landslide detectionDeep learningResNet-50Multilayer perceptronLoss function RLoss |
| spellingShingle | Xuqing Ren Xu Wu Donghao Zhai Xiangpeng Wang Ningbo He Mehreen Tarif ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture Scientific Reports Landslide detection Deep learning ResNet-50 Multilayer perceptron Loss function RLoss |
| title | ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture |
| title_full | ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture |
| title_fullStr | ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture |
| title_full_unstemmed | ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture |
| title_short | ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture |
| title_sort | resm fusionnet for efficient landslide detection algorithm with a hybrid architecture |
| topic | Landslide detection Deep learning ResNet-50 Multilayer perceptron Loss function RLoss |
| url | https://doi.org/10.1038/s41598-025-98230-6 |
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