Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach
Sustainable land-use planning and catastrophe risk reduction depend critically on landslide susceptibility mapping. The complex, nonlinear interconnections of environmental and human elements cause terrain instability and challenge conventional prediction methods. In this work, we offer a DeepLabV3+...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11030867/ |
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| author | Huyong Yan Asad Khan Ahsan Jamil Belkendil Abdeldjalil Taoufik Saidani Nazih Y. Rebouh |
| author_facet | Huyong Yan Asad Khan Ahsan Jamil Belkendil Abdeldjalil Taoufik Saidani Nazih Y. Rebouh |
| author_sort | Huyong Yan |
| collection | DOAJ |
| description | Sustainable land-use planning and catastrophe risk reduction depend critically on landslide susceptibility mapping. The complex, nonlinear interconnections of environmental and human elements cause terrain instability and challenge conventional prediction methods. In this work, we offer a DeepLabV3+-based deep learning framework coupled with geographic information systems and multicriteria decision making methods for spatial prediction of landslide risk, over the Dubai coastal and urban region (covering approximately 4000 km<sup>2</sup>). The approach uses an annotated dataset for semantic segmentation and high-resolution satellite images from the Mohammed Bin Rashid Space Center. On Google Colab with GPU acceleration, the model is trained and verified and then further improved for computational efficiency on a Mac M1 machine. Our results show an overall accuracy of 91.3%, a mean intersection over union of 82.5%, and an F1-score of 88.4%, demonstrating strong classification performance throughout a range of land cover types. The confusion matrix analysis highlights strong segmentation accuracy for water bodies (94.2%) and structures (92.4%). Considerable misclassification between roadways and unpaved terrain results from spectral similarities. Furthermore, the perclass Dice Coefficient analysis confirms that the model can efficiently discriminate intricate topographical patterns. Especially in fast-expanding areas such as Dubai, UAE, it provides a scalable solution for landslide susceptibility mapping, catastrophe risk management, and sustainable urban design. Future work will explore multisensor data fusion, real-time inference, and applying explainable artificial intelligence techniques to enhance model interpretability in dynamic terrain settings. |
| format | Article |
| id | doaj-art-a70d5fc685f54cd9b6532ec5504aaa68 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-a70d5fc685f54cd9b6532ec5504aaa682025-08-20T02:37:42ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118152221523510.1109/JSTARS.2025.357882211030867Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ ApproachHuyong Yan0Asad Khan1https://orcid.org/0000-0002-1261-0418Ahsan Jamil2https://orcid.org/0000-0001-6855-4147Belkendil Abdeldjalil3https://orcid.org/0000-0001-9247-848XTaoufik Saidani4Nazih Y. Rebouh5https://orcid.org/0000-0002-8621-6595School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing, ChinaMetaverse Research Institute, School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, PR ChinaDepartment of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, USACenter for Research in Territory Planning (CRAT), Zouaghi Slimane Campus, Constantine, AlgeriaCenter for Scientific Research and Entrepreneurship, Northern Border University, Arar, Saudi ArabiaDepartment of Environmental Management, Institute of Environmental Engineering, RUDN University, Moscow, RussiaSustainable land-use planning and catastrophe risk reduction depend critically on landslide susceptibility mapping. The complex, nonlinear interconnections of environmental and human elements cause terrain instability and challenge conventional prediction methods. In this work, we offer a DeepLabV3+-based deep learning framework coupled with geographic information systems and multicriteria decision making methods for spatial prediction of landslide risk, over the Dubai coastal and urban region (covering approximately 4000 km<sup>2</sup>). The approach uses an annotated dataset for semantic segmentation and high-resolution satellite images from the Mohammed Bin Rashid Space Center. On Google Colab with GPU acceleration, the model is trained and verified and then further improved for computational efficiency on a Mac M1 machine. Our results show an overall accuracy of 91.3%, a mean intersection over union of 82.5%, and an F1-score of 88.4%, demonstrating strong classification performance throughout a range of land cover types. The confusion matrix analysis highlights strong segmentation accuracy for water bodies (94.2%) and structures (92.4%). Considerable misclassification between roadways and unpaved terrain results from spectral similarities. Furthermore, the perclass Dice Coefficient analysis confirms that the model can efficiently discriminate intricate topographical patterns. Especially in fast-expanding areas such as Dubai, UAE, it provides a scalable solution for landslide susceptibility mapping, catastrophe risk management, and sustainable urban design. Future work will explore multisensor data fusion, real-time inference, and applying explainable artificial intelligence techniques to enhance model interpretability in dynamic terrain settings.https://ieeexplore.ieee.org/document/11030867/Coastal monitoringDeepLabV3+geographic information systems (GISs)landslide susceptibility mappingmultisensor data fusionremote sensing |
| spellingShingle | Huyong Yan Asad Khan Ahsan Jamil Belkendil Abdeldjalil Taoufik Saidani Nazih Y. Rebouh Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Coastal monitoring DeepLabV3+ geographic information systems (GISs) landslide susceptibility mapping multisensor data fusion remote sensing |
| title | Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach |
| title_full | Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach |
| title_fullStr | Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach |
| title_full_unstemmed | Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach |
| title_short | Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach |
| title_sort | deep learning based spatial prediction of landslide risk in coastal areas using gis and multicriteria decision making a deeplabv3 approach |
| topic | Coastal monitoring DeepLabV3+ geographic information systems (GISs) landslide susceptibility mapping multisensor data fusion remote sensing |
| url | https://ieeexplore.ieee.org/document/11030867/ |
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