{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T05:10:02Z","timestamp":1778389802167,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Landslides are widespread geohazards in mountainous regions and pose serious threats to human safety, infrastructure, and ecosystems. Accurate detection from high-resolution optical remote sensing imagery remains challenging because landslide targets often exhibit irregular morphology, large scale variation, weak boundaries, and strong background interference. To address these issues, this study proposes L-SAINet, a shape-adaptive and inner-scale interaction network for landslide detection in complex remote sensing scenarios. Built on a lightweight one-stage detection framework, the proposed method introduces an L-SAI module that integrates adaptive deformable convolution, channel\u2013spatial attention, and inner-scale feature interaction. The shape-adaptive branch improves geometric alignment for irregular and elongated landslide bodies, while the attention branch enhances semantic discrimination under heterogeneous background conditions. The two branches are further fused at the same feature scale to construct a more unified landslide representation. Experiments on the Bijie Landslide Remote Sensing Dataset show that L-SAINet consistently outperforms the baseline detector and single-branch variants in Precision, Recall, mAP@0.5, and mAP@0.5:0.95. Additional analyses based on precision\u2013recall curves, confusion matrices, convergence behavior, model complexity, and representative complex-scene examples further confirm its effectiveness and robustness. The results demonstrate that jointly modeling geometric adaptability and semantic refinement is an effective strategy for landslide detection in complex mountain environments.<\/jats:p>","DOI":"10.3390\/s26092812","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T14:35:53Z","timestamp":1777559753000},"page":"2812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["L-SAINet: A Shape-Adaptive and Inner-Scale Interaction Network for Landslide Detection in Complex Remote Sensing Scenarios"],"prefix":"10.3390","volume":"26","author":[{"given":"Yanchang","family":"Jia","sequence":"first","affiliation":[{"name":"College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1864-4743","authenticated-orcid":false,"given":"Shuyan","family":"Hua","sequence":"additional","affiliation":[{"name":"College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9688-7886","authenticated-orcid":false,"given":"Tong","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1130\/G33217.1","article-title":"Global patterns of loss of life from landslides","volume":"40","author":"Petley","year":"2012","journal-title":"Geology"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Highland, L.M., and Bobrowsky, P. (2008). The Landslide Handbook\u2014A Guide to Understanding Landslides, U.S. Geological Survey Circular 1325.","DOI":"10.3133\/cir1325"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1038\/ngeo1806","article-title":"The landslide story","volume":"6","author":"Huang","year":"2013","journal-title":"Nat. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.earscirev.2016.08.011","article-title":"Landslides in a changing climate","volume":"162","author":"Gariano","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.enggeo.2008.03.010","article-title":"Spatial data for landslide susceptibility, hazard, and vulnerability assessment","volume":"102","author":"Castellanos","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_6","first-page":"1609","article-title":"Landslide early warning systems: Toolbox and general concepts","volume":"14","author":"Intrieri","year":"2017","journal-title":"Landslides"},{"key":"ref_7","first-page":"1370","article-title":"Rapidly evolving controls of landslides after a strong earthquake and implications for hazard assessments","volume":"46","author":"Fan","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide inventory maps: New tools for an old problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth-Sci. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote sensing for landslide investigations: An overview of recent developments and perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Miao, Z., Xiong, Y., Cheng, Z., Wu, B., Wang, W., and Peng, Z. (2025). Quantifying root cohesion spatial heterogeneity using remote sensing for improved landslide susceptibility modeling: A case study of Caijiachuan landslides. Sensors, 25.","DOI":"10.3390\/s25134221"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"56","article-title":"Semi-automatic recognition and mapping of rainfall-induced shallow landslides using aerial photographs","volume":"134","author":"Mondini","year":"2011","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.geomorph.2009.10.004","article-title":"Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods","volume":"116","author":"Martha","year":"2010","journal-title":"Geomorphology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s12145-015-0217-3","article-title":"An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan","volume":"8","author":"Friedl","year":"2015","journal-title":"Earth Sci. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1109\/JSTARS.2018.2803784","article-title":"Landslide inventory mapping from bitemporal high-resolution remote sensing images using change detection and multiscale segmentation","volume":"11","author":"Lv","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth-Sci. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4654","DOI":"10.1109\/TGRS.2020.3015826","article-title":"Landslide recognition by deep convolutional neural networks and change detection","volume":"59","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhao, C., Xi, J., Lu, Z., Ji, S., and Chen, L. (2022). Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14215517"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Prakash, N., Manconi, A., and Loew, S. (2020). Mapping landslides on EO data: Performance of deep learning vs. traditional ML. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-11876"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104249","DOI":"10.1016\/j.catena.2019.104249","article-title":"Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment","volume":"186","author":"Sameen","year":"2019","journal-title":"Catena"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Trinidad, M., and Momayez, M. (2025). Machine learning in slope stability: A review with implications for landslide hazard assessment. GeoHazards, 6.","DOI":"10.3390\/geohazards6040067"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1109\/TGRS.2019.2953119","article-title":"Object detection in high-resolution remote sensing imagery based on convolutional neural networks with suitable object scale features","volume":"58","author":"Dong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2023.3312347","article-title":"Remote sensing object detection meets deep learning: A meta-review of challenges and advances","volume":"11","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pham, M.T., Courtrai, L., Friguet, C., Lef\u00e8vre, S., and Baussard, A. (2020). YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12152501"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yang, Z., Guan, W., Xiao, L., and Chen, H. (2024). Few-shot object detection in remote sensing images via data clearing and stationary meta-learning. Sensors, 24.","DOI":"10.3390\/s24123882"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, C., Wang, Y., Cao, Q., Zhang, C., and Cheng, A. (2025). Rethinking adaptive contextual information and multi-scale feature fusion for small-object detection in UAV imagery. Sensors, 25.","DOI":"10.3390\/s25237312"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, F., and Wang, X. (2025). HSF-YOLO: A multi-scale and gradient-aware network for small object detection in remote sensing images. Sensors, 25.","DOI":"10.3390\/s25144369"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Valente, J., Van Der Voort, M., and Tekinerdogan, B. (2021). Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review. Remote Sens., 13.","DOI":"10.3390\/rs13152965"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 15\u201320). Deformable ConvNets v2: More deformable, better results. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S.Z. (2018, January 18\u201322). Single-shot refinement neural network for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00442"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 15\u201320). Libra R-CNN: Towards balanced learning for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bell, S., Lawrence Zitnick, C., Bala, K., and Girshick, R. (2016, January 27\u201330). Inside\u2013outside net: Detecting objects in context with skip pooling and recurrent neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref_41","unstructured":"Luo, W., Li, Y., Urtasun, R., and Zemel, R. (2016). Understanding the effective receptive field in deep convolutional neural networks. Advances in Neural Information Processing Systems 29 (NeurIPS 2016), NIPS Foundation."},{"key":"ref_42","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-scale context aggregation by dilated convolutions. Proceedings of the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tian, Z., Fang, Y., Fang, X., Ma, Y., and Li, H. (2024). A large-scale building unsupervised extraction method leveraging airborne LiDAR point clouds and remote sensing images based on a dual P-snake model. Sensors, 24.","DOI":"10.3390\/s24237503"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/MGRS.2021.3136100","article-title":"Deep learning and Earth observation to support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities","volume":"10","author":"Persello","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2812\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T04:25:23Z","timestamp":1778387123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,30]]},"references-count":45,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26092812"],"URL":"https:\/\/doi.org\/10.3390\/s26092812","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,30]]}}}