Advanced Search

Indexed by SCI、CA、РЖ、PA、CSA、ZR、etc .

Volume 37 Issue 2
Apr 2026
Turn off MathJax
Article Contents
Journal of Earth Science  > 2026  >  37(2): 685-702.
Xuyang Xiang, Wenping Gong, Fumeng Zhao, Zhan Cheng, Lei Wang. Earthquake-Induced Landslide Mapping in Mountainous Areas Using a Semantic Segmentation Model Combined with a Dual Feature Pyramid. Journal of Earth Science, 2026, 37(2): 685-702. doi: 10.1007/s12583-023-1956-6
Citation: Xuyang Xiang, Wenping Gong, Fumeng Zhao, Zhan Cheng, Lei Wang. Earthquake-Induced Landslide Mapping in Mountainous Areas Using a Semantic Segmentation Model Combined with a Dual Feature Pyramid. Journal of Earth Science, 2026, 37(2): 685-702. doi: 10.1007/s12583-023-1956-6
shu

Earthquake-Induced Landslide Mapping in Mountainous Areas Using a Semantic Segmentation Model Combined with a Dual Feature Pyramid

doi: 10.1007/s12583-023-1956-6
  • 1.

    Faculty of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China

  • 2.

    Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati OH 45221, USA

More Information
  • Corresponding author: Wenping Gong, wenpinggong@cug.edu.cn
  • Received Date: 16 Aug 2023
  • Accepted Date: 22 Nov 2023
  • Issue Publish Date: 30 Apr 2026
    Abstract

  • Landslides are widely distributed in mountainous regions around the world. Rapid mapping of earthquake-induced landslides in mountainous areas plays a crucial role in post-disaster assessment and rescue planning. In mountainous areas, it is challenging to identify small landslides using existing landslide mapping methods accurately. To address this challenge, this paper proposes a dual feature pyramid-based UNet (DFPU-Net) model, which utilizes the VGG16 model as the backbone feature extraction network. Meanwhile, two modified pyramid-structured modules, in terms of the atrous spatial pyramid pooling (ASPP) module and pyramid pooling module (PPM), are integrated into the backbone feature extraction network. Furthermore, the features of landslides extracted by the enhanced feature extraction network are screened by the deconvolution layers and convolutional block attention module (CBAM). To demonstrate the effectiveness of the proposed model, landslides in Grand'Anse and Sud departments, induced by the 2021 Haiti Mw7.3 earthquake, are mapped as a case study. The inputs adopted in this case study are satellite optical images, slope map, and normalized difference vegetation index (NDVI) map collected in this study region, and the estimated Precision, Recall, F1 score, Accuracy, and IoU of the landslide mapping results are 89.10%, 74.20%, 80.97%, 95.32%, and 68.02% respectively, indicating the effectiveness of the proposed model. The comparisons conducted show that the proposed model yields a higher Recall value than the existing models of UNet, DeepLab V3+, and PSPNet, thus, the superiority of the proposed model over the existing models is demonstrated.

     

    • Conflict of Interest
    The authors declare that they have no conflict of interest.
    FullText(HTML)
  • loading
    • References(69)
  • Anshori, R. M., Samodra, G., Mardiatno, D., et al., 2022. Volunteered Geographic Information Mobile Application for Participatory Landslide Inventory Mapping. Computers & Geosciences, 161: 105073. https://doi.org/10.1016/j.cageo.2022.105073
    Bekaert, D. P. S., Handwerger, A. L., Agram, P., et al., 2020. InSAR-Based Detection Method for Mapping and Monitoring Slow-Moving Landslides in Remote Regions with Steep and Mountainous Terrain: An Application to Nepal. Remote Sensing of Environment, 249: 111983. https://doi.org/10.1016/j.rse.202 0.111983 doi: 10.1016/j.rse.2020.111983
    Berrar, D., 2019. Cross-Validation. Encyclopedia of Bioinformatics and Computational Biology, Academic Press, Oxford, 542–545
    Borghuis, A. M., Chang, K., Lee, H. Y., 2007. Comparison between Automated and Manual Mapping of Typhoon‐Triggered Landsli-des from SPOT‐5 Imagery. International Journal of Remote Sensing, 28(8): 1843–1856. https://doi.org/10.1080/01431160600935638
    Chen, L. C., Zhu, Y. K., Papandreou, G., et al., 2018. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Computer Vision–ECCV 2018. Springer, Cham
    Cheng, Z., Gong, W. P., Tang, H. M., et al., 2021. UAV Photogrammetry-Based Remote Sensing and Preliminary Assessment of the Behavior of a Landslide in Guizhou, China. Engineering Geology, 289: 106172. https://doi.org/10.1016/j.eng geo.2021.106172 doi: 10.1016/j.enggeo.2021.106172
    Chollet, F., 2017. Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21–26, 2017, Honolulu, HI, USA. IEEE: 1800–1807. https://doi.org/10.1109/cvpr.2017.195
    Cui, S., Ma, A. L., Zhang, L. P., et al., 2021. MAP-Net: SAR and Optical Image Matching via Image-Based Convolutional Network with Attention Mechanism and Spatial Pyramid Aggregated Pooling. IEEE Transactions on Geoscience and Remote Sensing, 60: 1000513. https://doi.org/10.1109/tgrs.2021.3066432
    Du, B. W., Zhao, Z. R., Hu, X., et al., 2021. Landslide Susceptibility Prediction Based on Image Semantic Segmentation. Computers & Geosciences, 155: 104860. https://doi.org/10.1016/j.cageo.2021.104860
    Esgario, J. G. M., de Castro, P. B. C., Tassis, L. M., et al., 2022. An App to Assist Farmers in the Identification of Diseases and Pests of Coffee Leaves Using Deep Learning. Information Processing in Agriculture, 9(1): 38–47. https://doi.org/10.1016/j.inpa.2021.01.004
    Fan, X. Y., Tang, J. J., Tian, S. J., et al., 2020. Rainfall-Induced Rapid and Long-Runout Catastrophic Landslide on July 23, 2019 in Shuicheng, Guizhou, China. Landslides, 17(9): 2161–2171. https://doi.org/10.1007/s10346-020-01454-y
    Fang, Z. C., Wang, Y., Peng, L., et al., 2020. Integration of Convolutional Neural Network and Conventional Machine Learning Classifiers for Landslide Susceptibility Mapping. Computers & Geosciences, 139: 104470. https://doi.org/10.1016/j.cageo.2020.104470
    Fanos, A. M., Pradhan, B., Mansor, S., et al., 2018. A Hybrid Model Using Machine Learning Methods and GIS for Potential Rockfall Source Identification from Airborne Laser Scanning Data. Landslides, 15(9): 1833–1850. https://doi.org/10.1007/s10346-018-0990-4
    Feng, P. F., Li, C. D., Zhang, S., et al., 2024. Integrating Shipborne Images with Multichannel Deep Learning for Landslide Detection. Journal of Earth Science, 35(1): 296–300. https://doi.org/10.1007/s12583-023-1957-5
    Gao, D. X., Li, K., Cai, Y. C., et al., 2024. Landslide Displacement Prediction Based on Time Series and PSO-BP Model in Three Georges Reservoir, China. Journal of Earth Science, 35(3): 1079–1082. https://doi.org/10.1007/s12583-021-1575-z
    Gong, W. P., Juang, C. H., Wasowski, J., 2021. Geohazards and Human Settlements: Lessons Learned from Multiple Relocation Events in Badong, China–Engineering Geologist's Perspective. Engineering Geology, 285: 106051. https://doi.org/10.1016/j.enggeo.2021.106051
    Guzzetti, F., Mondini, A. C., Cardinali, M., et al., 2012. Landslide Inventory Maps: New Tools for an Old Problem. Earth-Science Reviews, 112(1/2): 42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
    He, X., Zhou, Y., Zhao, J. Q., et al., 2022. Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation. IEEE Transactions on Geoscience and Remote Sensing, 60: 4408715. https://doi.org/10.1109/tgrs.2022.3144165
    Huang, Y. D., Xie, C. C., Li, T., et al., 2023. An Open-Accessed Inventory of Landslides Triggered by the M S6.8 Luding Earthquake, China on September 5, 2022. Earthquake Research Advances, 3(1): 100181. https://doi.org/10.1016/j.eqrea.2022.100181
    Jebur, M. N., Pradhan, B., Tehrany, M. S., 2015. Manifestation of LiDAR-Derived Parameters in the Spatial Prediction of Landslides Using Novel Ensemble Evidential Belief Functions and Support Vector Machine Models in GIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2): 674–690. https://doi.org/10.1109/jstars.2014.2341276
    Ji, S. P., Yu, D. W., Shen, C. Y., et al., 2020. Landslide Detection from an Open Satellite Imagery and Digital Elevation Model Dataset Using Attention Boosted Convolutional Neural Networks. Landslides, 17(6): 1337–1352. https://doi.org/10.1007/s10346-020-01353-2
    John, D., Zhang, C., 2022. An Attention-Based U-Net for Detecting Deforestation within Satellite Sensor Imagery. International Journal of Applied Earth Observation and Geoinformation, 107: 102685. https://doi.org/10.1016/j.jag.2022.102685
    Li, H. J., He, Y. S., Xu, Q., et al., 2022. Detection and Segmentation of Loess Landslides via Satellite Images: A Two-Phase Framework. Landslides, 19(3): 673–686. https://doi.org/10.1007/s10346-021-01789-0
    Liang, T. L., Glossner, J., Wang, L., et al., 2021. Pruning and Quantization for Deep Neural Network Acceleration: A Survey. Neurocomputing, 461: 370–403. https://doi.org/10.1016/j.neuco m.2021.07.045 doi: 10.1016/j.neucom.2021.07.045
    Long, Y. J., Li, W. L., Huang, R. Q., et al., 2023. A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin, China. Journal of Earth Science, 34(2): 316–329. https://doi.org/10.1007/s12583-021-1525-9
    Lu, P., Shi, W. Y., Li, Z. B., 2021. Landslide Mapping from Planet Scope Images Using Improved Region-Based Level Set Evolution. IEEE Geoscience and Remote Sensing Letters, 19: 2502505. https://doi.org/10.1109/lgrs.2021.3122964
    Lu, P., Stumpf, A., Kerle, N., et al., 2011. Object-Oriented Change Detection for Landslide Rapid Mapping. IEEE Geoscience and Remote Sensing Letters, 8(4): 701–705. https://doi.org/10.1109/lgrs.2010.2101045
    Lundine, M. A., Brothers, L. L., Trembanis, A. C., 2023. Deep Learning for Pockmark Detection: Implications for Quantitative Seafloor Characterization. Geomorphology, 421: 108524. https://doi.org/10.1016/j.geomorph.2022.108524
    Lyu, Z. Y., Wang, F. J., Sun, W. W., et al., 2022. Landslide Inventory Mapping on VHR Images via Adaptive Region Shape Similarity. IEEE Transactions on Geoscience and Remote Sensing, 60: 5630211. https://doi.org/10.1109/tgrs.2022.3204834
    Middleton, M., Heikkonen, J., Nevalainen, P., et al., 2020. Machine Learning-Based Mapping of Micro-Topographic Earthquake-Induced Paleo-Pulju Moraines and Liquefaction Spreads from a Digital Elevation Model Acquired through Laser Scanning. Geomorphology, 358: 107099. https://doi.org/10.1016/j.geomorph.2020.107099
    Mondini, A. C., Guzzetti, F., Reichenbach, P., et al., 2011. Semi-Automatic Recognition and Mapping of Rainfall Induced Shallow Landslides Using Optical Satellite Images. Remote Sensing of Environment, 115(7): 1743–1757. https://doi.org/10.1016/j.rse.2011.03.006
    Morales, B., Lizama, E., Somos-Valenzuela, M., et al., 2023. Earthquake-Induced Landslides Coupled to Fluvial Incision in Andean Patagonia: Inferring Their Effects on Landscape at Geological Time Scales. Geomorphology, 434: 108731. https://doi.org/10.1016/j.geomorph.2023.108731
    Muthu, K., Petrou, M., 2007. Landslide-Hazard Mapping Using an Expert System and a GIS. IEEE Transactions on Geoscience and Remote Sensing, 45(2): 522–531. https://doi.org/10.1109/tgrs.2006.885404
    Muthu, K., Petrou, M., Tarantino, C., et al., 2008. Landslide Possibility Mapping Using Fuzzy Approaches. IEEE Transactions on Geoscience and Remote Sensing, 46(4): 1253–1265. https://doi.org/10.1109/tgrs.2007.912441
    Noh, H., Hong, S., Han, B., 2015. Learning Deconvolution Network for Semantic Segmentation. 2015 IEEE International Conference on Computer Vision (ICCV). December 7–13, 2015, Santiago. https://doi.org/10.1109/icc v.2015.178
    Paoletti, M. E., Haut, J. M., Plaza, J., et al., 2018. A New Deep Convolutional Neural Network for Fast Hyperspectral Image Classification. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 120–147. https://doi.org/10.1016/j.isprsjprs.2017. 11.021 doi: 10.1016/j.isprsjprs.2017.11.021
    Pareek, N., Pal, S., Sharma, M. L., et al., 2013. Study of Effect of Seismic Displacements on Landslide Susceptibility Zonation (LSZ) in Garhwal Himalayan Region of India Using GIS and Remote Sensing Techniques. Computers & Geosciences, 61: 50–63. https://doi.org/10.1016/j.cageo.2013.07.018
    Prakash, N., Manconi, A., Loew, S., 2020. Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sensing, 12(3): 346. https://doi.org/10.3390/rs12030346
    Rau, J. Y., Jhan, J. P., Rau, R. J., 2014. Semiautomatic Object-Oriented Landslide Recognition Scheme from Multisensor Optical Imagery and DEM. IEEE Transactions on Geoscience and Remote Sensing, 52(2): 1336–1349. https://doi.org/10.1109/tgrs.2013.2250293
    Ren, T. H., Gong, W. P., Gao, L., et al., 2022. An Interpretation Approach of Ascending-Descending SAR Data for Landslide Identification. Remote Sensing, 14(5): 1299. https://doi.org/10.3390/rs14051299
    Ronneberger, O., Fischer, P., Brox, T., 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Springer, Cham
    Stumpf, A., Kerle, N., 2011. Object-Oriented Mapping of Landslides Using Random Forests. Remote Sensing of Environment, 115(10): 2564–2577. https://doi.org/10.1016/j.rse.2011.05.013
    Su, Z. Y., Chow, J. K., Tan, P. S., et al., 2021. Deep Convolutional Neural Network-Based Pixel-Wise Landslide Inventory Mapping. Landslides, 18(4): 1421–1443. https://doi.org/10.1007/s10346-020-01557-6
    Sun, W. Y., Tian, Y. S., Mu, X. M., et al., 2017. Loess Landslide Inventory Map Based on GF-1 Satellite Imagery. Remote Sensing, 9(4): 314. https://doi.org/10.3390/rs9040314
    Tamkuan, N., Nagai, M., 2017. Fusion of Multi-Temporal Interferometric Coherence and Optical Image Data for the 2016 Kumamoto Earthquake Damage Assessment. ISPRS International Journal of Geo-Information, 6(7): 188. https://doi.org/10.3390/ijgi6070188
    Tang, H. M., Jia, H. B., Hu, X. L., et al., 2010. Characteristics of Landslides Induced by the Great Wenchuan Earthquake. Journal of Earth Science, 21(1): 104–113. https://doi.org/10.1007/s12583-010-0008-1
    Tang, H. M., Liu, X., Hu, X. L., et al., 2015. Evaluation of Landslide Mechanisms Characterized by High-Speed Mass Ejection and Long-Run-out Based on Events Following the Wenchuan Earthquake. Engineering Geology, 194: 12–24. https://doi.org/10.1016/j.enggeo.2015.01.004
    Tang, H. M., Wasowski, J., Juang, C. H., 2019. Geohazards in the Three Gorges Reservoir Area, China–Lessons Learned from Decades of Research. Engineering Geology, 261: 105267. https://doi.org/10.1016/j.enggeo.2019.105267
    Tian, J. -J., Li, T. -T., Pei, X. -J., et al., 2024. Experimental Study on Multistage Seismic Damage Process of Bedding Rock Slope: A Case Study of the Xinmo Landslide. Journal of Earth Science, 35(5): 1594–1612. https://doi.org/10.1007/s12583-023-1829-z
    Travelletti, J., Delacourt, C., Allemand, P., et al., 2012. Correlation of Multi-Temporal Ground-Based Optical Images for Landslide Monitoring: Application, Potential and Limitations. ISPRS Journal of Photogrammetry and Remote Sensing, 70: 39–55. https://doi.org/10.1016/j.isprsjprs.2012.03.007
    Wang, P. Q., Chen, P. F., Yuan, Y., et al., 2018. Understanding Convolution for Semantic Segmentation. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). March 12–15, 2018, Lake Tahoe. https://doi.org/10.1109/wacv.2018.00163
    Whitworth, M. R. Z., Giardina, G., Penney, C., et al., 2022. Lessons for Remote Post-Earthquake Reconnaissance from the 14 August 2021 Haiti Earthquake. Frontiers in Built Environment, 8: 873212. https://doi.org/10.3389/fbuil.2022.873212
    Williams, J. G., Rosser, N. J., Kincey, M. E., et al., 2018. Satellite-Based Emergency Mapping Using Optical Imagery: Experience and Reflections from the 2015 Nepal Earthquakes. Natural Hazards and Earth System Sciences, 18(1): 185–205. https://doi.org/10.5194/nhess-18-185-2018
    Woo, S., Park, J., Lee, J. Y., et al., 2018. CBAM: Convolutional Block Attention Module. Computer Vision–ECCV 2018. Springer, Cham
    Wu, W. Y., Xu, C., Wang, X. Q., et al., 2020. Landslides Triggered by the 3 August 2014 Ludian (China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution. Journal of Earth Science, 31(4): 853–866. https://doi.org/10.1007/s12583-020-1297-7
    Xu, G. S., Wang, Y., Wang, L. Z., et al., 2022. Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions with Mountainous Terrain: An Application to Brazil. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 2644–2659 doi: 10.1109/JSTARS.2022.3161383
    Xu, Q., Li, W. L., Ju, Y. Z., et al., 2020. Multitemporal UAV-Based Photogrammetry for Landslide Detection and Monitoring in a Large Area: A Case Study in the Heifangtai Terrace in the Loess Plateau of China. Journal of Mountain Science, 17(8): 1826–1839. https://doi.org/10.1007/s11629-020-6064-9
    Xu, R. D., Tao, Y. T., Lu, Z. Y., et al., 2018. Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification. Remote Sensing, 10(10): 1602. https://doi.org/10.3390/rs10101602
    Yang, M. K., Yu, K., Zhang, C., et al., 2018. DenseASPP for Semantic Segmentation in Street Scenes. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18–23, 2018, Salt Lake City. https://doi.org/10.1109/cvpr.2018.00388
    Yao, G. L., Zhou, W. L., Liu, M. Z., et al., 2021. An Empirical Study of the Convolution Neural Networks Based Detection on Object with Ambiguous Boundary in Remote Sensing Imagery—A Case of Potential Loess Landslide. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 323–338
    Yi, Y. N., Zhang, W. C., 2020. A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection from Single-Temporal RapidEye Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 6166–6176 doi: 10.1109/JSTARS.2020.3028855
    Yu, B., Chen, F., Xu, C., 2020. Landslide Detection Based on Contour-Based Deep Learning Framework in Case of National Scale of Nepal in 2015. Computers & Geosciences, 135: 104388. https://doi.org/10.1016/j.cageo.2019.104388
    Yu, B., Xu, C., Chen, F., et al., 2022. HADeenNet: A Hierarchical-Attention Multi-Scale Deconvolution Network for Landslide Detection. International Journal of Applied Earth Observation and Geoinformation, 111: 102853. https://doi.org/10.1016/j.jag.2022.102853
    Zhang, J. S., Xing, M. D., Sun, G. C., et al., 2022. Vehicle Trace Detection in Two-Pass SAR Coherent Change Detection Images with Spatial Feature Enhanced Unet and Adaptive Augmentation. IEEE Transactions on Geoscience and Remote Sensing, 60: 5232415. https://doi.org/10.1109/tgrs.2022.3194903
    Zhang, Y. M., Zhou, C. L., Chang, F. L., et al., 2019. Multi-Resolution Attention Convolutional Neural Network for Crowd Counting. Neurocomputing, 329: 144–152. https://doi.org/10.1016/j.neucom.2018.10.058
    Zhao, B., Wang, Y. S., Li, W. L., et al., 2022. Evaluation of Factors Controlling the Spatial and Size Distributions of Landslides, 2021 Nippes Earthquake, Haiti. Geomorphology, 415: 108419. https://doi.org/10.1016/j.geomorph.2022.108419
    Zhao, H. S., Shi, J. P., Qi, X. J., et al., 2017. Pyramid Scene Parsing Network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21–26, 2017, Honolulu. https://doi.org/10.1109/cvpr.2017.660
    Zhong, C., Liu, Y., Gao, P., et al., 2020. Landslide Mapping with Remote Sensing: Challenges and Opportunities. International Journal of Remote Sensing, 41(4): 1555–1581 doi: 10.1080/01431161.2019.1672904
    Zhou, H. F., Ye, F., Fu, W. X., et al., 2024. Dynamic Effect of Landslides Triggered by Earthquake: A Case Study in Moxi Town of Luding County, China. Journal of Earth Science, 35(1): 221–234. https://doi.org/10.1007/s12583-022-1806-y
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(12)  / Tables(7)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views(815) PDF downloads(58) Cited by()
    Proportional views
    Related

    Copyright © 2013-2020 Journal of Earth Science 鄂ICP备15021562号-2

    Tel: +86-27-67885075 Fax: +86-27-67885075 E-mail: xbb@cug.edu.cn

    Address: Editorial Office of Journal, China University of Geosciences, Yujiashan, Wuhan, Hubei 430074, P. R. China

    Supported by: Beijing Renhe Information Technology Co. Ltd  E-mail: info@rhhz.net  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return