{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:06:12Z","timestamp":1790075172981,"version":"4.0.1"},"reference-count":48,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Office of the Decision-Making Advisory Committee of Shanxi Provincial People\u2019s Government"},{"name":"the Shanxi Earthquake Administration"},{"name":"Shanxi Academy of Social Sciences","award":["SXFZJZZS2601"],"award-info":[{"award-number":["SXFZJZZS2601"]}]},{"award":["SXFZJZZS2601"],"award-info":[{"award-number":["SXFZJZZS2601"]}],"id":[{"id":"https:\/\/ror.org\/02mq91505","id-type":"ROR","asserted-by":"publisher"}]},{"name":"the National Institute of Natural Hazards, Ministry of Emergency Management of China","award":["ZDJ2025-21"],"award-info":[{"award-number":["ZDJ2025-21"]}],"id":[{"id":"https:\/\/ror.org\/02e6b1s72","id-type":"ROR","asserted-by":"crossref"}]},{"award":["ZDJ2025-21"],"award-info":[{"award-number":["ZDJ2025-21"]}],"id":[{"id":"https:\/\/ror.org\/02e6b1s72","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Landslides are characterized by sudden occurrence, severe destructiveness, and widespread spatial distribution. Therefore, rapid and accurate landslide detection is essential for reducing infrastructure damage and safeguarding human lives. Conventional landslide monitoring methods are generally time-consuming, labor-intensive, and inefficient, while existing deep learning-based landslide detection methods remain limited in multiscale spatial structure representation, temporal sequence modeling, and spatiotemporal feature fusion. To address these limitations, this study proposes ConFormer-Net, a spatiotemporal landslide detection model that integrates convolutional neural networks (CNNs) with a Transformer architecture for landslide detection from multi-temporal synthetic aperture radar (SAR) imagery. The proposed model adopts a hybrid architecture and incorporates a cross-attention mechanism. Specifically, a dilated convolutional network is employed to extract local spatial features of landslides, while a Transformer encoding module models the temporal dependencies among multi-temporal SAR observations. The extracted spatial and temporal features are subsequently integrated through the cross-attention mechanism to achieve accurate landslide detection. The overall detection performance of ConFormer-Net was first evaluated using samples from different regions in the publicly available Sen12Landslides dataset. The proposed model was then compared with CNN, CNN-LSTM, ConvLSTM, GRU, CNN3D and ResNet50 models. The results demonstrate that ConFormer-Net achieved the best overall performance, with an F1-score of 95.27%, an accuracy of 95.29%, a precision of 96.79%, and a recall of 93.79%. These results indicate that ConFormer-Net enables highly accurate landslide detection while maintaining moderate model complexity, demonstrating its effectiveness for landslide detection from multi-temporal SAR imagery.<\/jats:p>","DOI":"10.3390\/s26185943","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:21:48Z","timestamp":1790072508000},"page":"5943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ConFormer-Net: Spatiotemporal Modeling for Landslide Detection Using Multi-Temporal SAR Data"],"prefix":"10.3390","volume":"26","author":[{"given":"Shaofei","family":"Lan","sequence":"first","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"},{"name":"Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Daming","family":"Wu","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"},{"name":"Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Peng","family":"Lu","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"},{"name":"Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Beinan","family":"Guo","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Zixiao","family":"Li","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1126\/science.1143308","article-title":"Assessing landslide hazards","volume":"316","author":"Keefer","year":"2007","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Geertsema, M., Highland, L., and Vaugeouis, L. (2009). Environmental impact of landslides. Landslides\u2013Disaster Risk Reduction, Springer.","DOI":"10.1007\/978-3-540-69970-5_31"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2851","DOI":"10.1007\/s10346-024-02451-1","article-title":"Landslides in a changing world","volume":"22","year":"2025","journal-title":"Landslides"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1002\/esp.5816","article-title":"The effect of location and geometric properties of landslides caused by rainstorms and earthquakes","volume":"49","author":"Qiu","year":"2024","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s12665-023-11347-7","article-title":"Study of the spatial distribution and the temporal trend of landslide disasters that occurred in the Nepal Himalayas from 2011 to 2020","volume":"83","author":"KC","year":"2024","journal-title":"Environ. Earth Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cageo.2014.10.007","article-title":"Contour Connection Method for automated identification and classification of landslide deposits","volume":"74","author":"Leshchinsky","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s10346-014-0482-0","article-title":"Coupling of GPS\/GNSS and radar interferometric data for a 3D surface displacement monitoring of landslides","volume":"12","author":"Komac","year":"2015","journal-title":"Landslides"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s10346-018-1069-y","article-title":"Visual interpretation of stereoscopic NDVI satellite images to map rainfall-induced landslides","volume":"16","author":"Fiorucci","year":"2019","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103574","DOI":"10.1016\/j.earscirev.2021.103574","article-title":"Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future","volume":"216","author":"Mondini","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10346-020-01475-7","article-title":"Identification and monitoring landslides in Longitudinal Range-Gorge Region with InSAR fusion integrated visibility analysis","volume":"18","author":"Guo","year":"2021","journal-title":"Landslides"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2023RG000821","DOI":"10.1029\/2023RG000821","article-title":"Synthetic aperture radar for geosciences","volume":"62","author":"Meng","year":"2024","journal-title":"Rev. Geophys."},{"key":"ref_12","first-page":"102812","article-title":"A new algorithm for landslide dynamic monitoring with high temporal resolution by Kalman filter integration of multiplatform time-series InSAR processing","volume":"110","author":"Cai","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2014.09.029","article-title":"Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis","volume":"156","author":"Sun","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"115231","DOI":"10.1016\/j.rse.2026.115231","article-title":"Unveiling the long-term cascading effects of the 2018 Baige landslide and subsequent outburst flood with satellite radar observations","volume":"334","author":"Chen","year":"2026","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s10346-022-01982-9","article-title":"Investigating slow-moving shallow soil landslides using Sentinel-1 InSAR data in Gisborne, New Zealand","volume":"20","author":"Cook","year":"2023","journal-title":"Landslides"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107113","DOI":"10.1016\/j.rineng.2025.107113","article-title":"Slope deformation monitoring and prediction based on InSAR and deep learning model","volume":"28","author":"Deng","year":"2025","journal-title":"Results Eng."},{"key":"ref_17","first-page":"1","article-title":"Sentinel-1 SAR-based globally distributed co-seismic landslide detection by deep neural networks","volume":"2025","author":"Nava","year":"2025","journal-title":"Geosci. Model Dev. Discuss."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4654","DOI":"10.1109\/TGRS.2020.3015826","article-title":"Landslide recognition by deep convolutional neural network and change detection","volume":"59","author":"Shi","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114580","DOI":"10.1016\/j.rse.2024.114580","article-title":"An interpretable attention-based deep learning method for landslide prediction based on multi-temporal InSAR time series: A case study of Xinpu landslide in the TGRA","volume":"318","author":"Zhou","year":"2025","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, H., Yang, S., Wang, R., and Yang, K. (2024). Study on a landslide segmentation algorithm based on improved high-resolution networks. Appl. Sci., 14.","DOI":"10.3390\/app14156459"},{"key":"ref_21","first-page":"103516","article-title":"Automatic identification of active landslides over wide areas from time-series InSAR measurements using Faster RCNN","volume":"124","author":"Cai","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"114029","DOI":"10.1016\/j.rse.2024.114029","article-title":"Research on automatic recognition of active landslides using InSAR deformation under digital morphology: A case study of the Baihetan reservoir, China","volume":"304","author":"Liu","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_24","first-page":"145","article-title":"An early prediction model of regional landslide disasters in Fujian Province based on convolutional neural network","volume":"51","author":"Lihao","year":"2024","journal-title":"Hydrogeol. Eng. Geol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tai, Y.-H., Lo, C.-C., Tsai, F., and Chang, C.-P. (2026). Application of PSInSAR Monitoring for Large-Scale Landslide with Persistent Scatterers from Deep Learning Classification. Remote Sens., 18.","DOI":"10.3390\/rs18081181"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6166","DOI":"10.1109\/JSTARS.2020.3028855","article-title":"A new deep-learning-based approach for earthquake-triggered landslide detection from single-temporal RapidEye satellite imagery","volume":"13","author":"Yi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14629","DOI":"10.1038\/s41598-021-94190-9","article-title":"A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)","volume":"11","author":"Ghorbanzadeh","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5235","DOI":"10.1109\/JSTARS.2021.3079196","article-title":"Landslide detection using densely connected convolutional networks and environmental conditions","volume":"14","author":"Cai","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11417","DOI":"10.1109\/JSTARS.2021.3117975","article-title":"Landslide detection mapping employing CNN, ResNet, and DenseNet in the three gorges reservoir, China","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kumar, A., Misra, R., Singh, T., and Singh, V. (2022, January 23\u201324). Landslide detection with Ensemble-of-Deep learning classifiers trained with optimal features. Proceedings of the XVIII International Conference on Data Science and Intelligent Analysis of Information, Mumbai, India.","DOI":"10.1007\/978-3-031-16178-0_21"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1007\/s10064-025-04345-5","article-title":"Hybrid prediction for reservoir landslide deformation based on multi-source InSAR and deep learning","volume":"84","author":"Ruan","year":"2025","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6919","DOI":"10.1080\/01431161.2021.1947540","article-title":"Prediction of InSAR deformation time-series using a long short-term memory neural network","volume":"42","author":"Chen","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhu, H., Shen, Q., Li, J., Gulayozov, M., Shao, Y., Chen, B., and Zhu, C. (2026). Deformation Prediction and Potential Landslide Identification in the Upstream of Sarez Lake Based on Time Series InSAR and Stacked LSTM. Remote Sens., 18.","DOI":"10.3390\/rs18050811"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107446","DOI":"10.1016\/j.enggeo.2024.107446","article-title":"LiteTransNet: An interpretable approach for landslide displacement prediction using transformer model with attention mechanism","volume":"331","author":"Ge","year":"2024","journal-title":"Eng. Geol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cui, X., Chen, X., Zhou, J., and Lin, D. (2021, January 24\u201326). Transformer in image interpretation. Proceedings of the International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), Harbin, China.","DOI":"10.1117\/12.2631151"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"129688","DOI":"10.1016\/j.eswa.2025.129688","article-title":"A Hybrid Fusion Network using Convolutional Vision Transformers for Landslide Identification","volume":"298","author":"Sreelakshmi","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_37","first-page":"104954","article-title":"Multi-modal cross Swin transformer network for multi-label classification landslide detection with optical and SAR images of Luding","volume":"145","author":"Li","year":"2025","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bazi, Y., Bashmal, L., Rahhal, M.M.A., Dayil, R.A., and Ajlan, N.A. (2021). Vision transformers for remote sensing image classification. Remote Sens., 13.","DOI":"10.3390\/rs13030516"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1162\/coli_a_00445","article-title":"Position information in transformers: An overview","volume":"48","author":"Dufter","year":"2022","journal-title":"Comput. Linguist."},{"key":"ref_40","first-page":"4408820","article-title":"Transformer and CNN hybrid deep neural network for semantic segmentation of very-high-resolution remote sensing imagery","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"8020305","article-title":"When CNNs meet vision transformer: A joint framework for remote sensing scene classification","volume":"19","author":"Deng","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Perera, M.V., Bandara, W.G.C., Valanarasu, J.M.J., and Patel, V.M. (2022, January 17\u201322). Transformer-based SAR image despeckling. Proceedings of the IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884596"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1038\/s41597-025-06167-2","article-title":"A Spatio-Temporal Dataset for Satellite-Based Landslide Detection","volume":"12","author":"Heidler","year":"2025","journal-title":"Sci. Data"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s42452-021-04897-7","article-title":"A novel ResNet101 model based on dense dilated convolution for image classification","volume":"4","author":"Zhang","year":"2022","journal-title":"SN Appl. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Foumani, N.M., Tan, C.W., Webb, G.I., and Salehi, M. (2023). Improving position encoding of transformers for multivariate time series classification. arXiv.","DOI":"10.1007\/s10618-023-00948-2"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108786","DOI":"10.1016\/j.patcog.2022.108786","article-title":"Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery","volume":"130","author":"Qingyun","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lindsay, E., Ganer\u00f8d, A.J., Devoli, G., Reiche, J., Nordal, S., and Frauenfelder, R. (2025). Understanding landslide expression in SAR backscatter data: Global study and disaster response application. Remote Sens., 17.","DOI":"10.3390\/rs17193313"},{"key":"ref_48","first-page":"103795","article-title":"A framework for automated landslide dating utilizing SAR-derived parameters time-series, an enhanced transformer model, and dynamic thresholding","volume":"129","author":"Wang","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5943\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:29:19Z","timestamp":1790072959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5943"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,19]]},"references-count":48,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185943"],"URL":"https:\/\/doi.org\/10.3390\/s26185943","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,19]]}}}