{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T04:55:20Z","timestamp":1775019320609,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52308323"],"award-info":[{"award-number":["52308323"]}]},{"name":"National Natural Science Foundation of China","award":["U1934209"],"award-info":[{"award-number":["U1934209"]}]},{"name":"National Natural Science Foundation of China","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"National Natural Science Foundation of China","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["52308323"],"award-info":[{"award-number":["52308323"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["U1934209"],"award-info":[{"award-number":["U1934209"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["52308323"],"award-info":[{"award-number":["52308323"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["U1934209"],"award-info":[{"award-number":["U1934209"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Prompt detection of landslides is crucial for reducing the disaster risk and preventing landslides. However, landslide detection in practical applications still faces many challenges, such as the complexity of environmental backgrounds, the diversity of target scales, and the enormity of model weights. To address these issues, this paper proposes a lightweight LBE-YOLO model for real-time landslide detection. Firstly, a lightweight model is designed by integrating the GhostConv lightweight network with the YOLOv8n model. Inspired by GhostConv, this study innovatively designed the GhostC2f structure, which leverages linear thinking to further reduce the model parameters and computational burden. Additionally, the newly designed EGC2f structure, incorporating an attention mechanism, not only maintains the model\u2019s lightweight characteristics but also enhances the network\u2019s capability to extract valid information. Subsequently, the Path Aggregation Network (PAN) was optimized by introducing a bidirectional feature propagation mechanism to improve the model\u2019s feature fusion ability. Additionally, the Bijie landslide dataset was expanded through data augmentation strategies, thereby further improving the model\u2019s generalization capability. The experimental results indicate that, compared to the YOLOv8n model, the proposed model increased accuracy by 4.2%, while the model\u2019s weight and computational load were reduced by 32.0% and 35.5%, respectively. This verifies the superiority of the LBE-YOLO model in landslide target detection, which will help mitigate the impacts of natural disasters.<\/jats:p>","DOI":"10.3390\/rs16030534","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T09:56:34Z","timestamp":1706694994000},"page":"534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Optimizing Geo-Hazard Response: LBE-YOLO\u2019s Innovative Lightweight Framework for Enhanced Real-Time Landslide Detection and Risk Mitigation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3454-6939","authenticated-orcid":false,"given":"Yingjie","family":"Du","sequence":"first","affiliation":[{"name":"The School of Rail Transit, Soochow University, Suzhou 215006, China"},{"name":"School of Automotive and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China"}]},{"given":"Xiangyang","family":"Xu","sequence":"additional","affiliation":[{"name":"The School of Rail Transit, Soochow University, Suzhou 215006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2746-182X","authenticated-orcid":false,"given":"Xuhui","family":"He","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Central South University, Changsha 410075, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113288","DOI":"10.1016\/j.measurement.2023.113288","article-title":"A Depth Information-Based Method to Enhance Rainfall-Induced Landslide Deformation Area Identification","volume":"219","author":"Yuan","year":"2023","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s10346-022-01961-0","article-title":"Surface Multi-Hazard Effect of Underground Coal Mining","volume":"20","author":"Ma","year":"2023","journal-title":"Landslides"},{"key":"ref_3","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. 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