{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T02:41:06Z","timestamp":1769827266354,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Science and Technology, Science and 431 Engineering Research Board, New Delhi, India","award":["EEQ\/2022\/000812"],"award-info":[{"award-number":["EEQ\/2022\/000812"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide event detection poses a significant challenge in the remote sensing community, especially with the advancements in computer vision technology. As computational capabilities continue to grow, the traditional manual and partially automated methods of landslide recognition from remote sensing data are transitioning towards automatic approaches using deep learning algorithms. Moreover, attention models, encouraged by the human visual system, have emerged as crucial modules in diverse applications including natural hazard assessment. Therefore, we suggest a novel and intelligent generalized efficient layer aggregation network (GELAN) based on two prevalent attention modules, efficient channel attention (ECA) and convolutional block attention module (CBAM), to enrich landslide detection techniques from satellite images. CBAM and ECA are separately integrated into GELAN at different locations. The experiments are conducted using satellite images of the Nepal Himalayan region. Standard metrics such as precision, recall, F-score, and mAP (mean average precision) are considered for quantitative evaluation. GELANc+CBAM (F-score = 81.5%) demonstrates the best performance. This study underscores the suitability of the proposed approach in up-to-date inventory creation and accurate landslide mapping for disaster recovery and response efforts. Moreover, it contributes to developing early prediction models for landslide hazards.<\/jats:p>","DOI":"10.3390\/rs16142598","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T15:05:51Z","timestamp":1721142351000},"page":"2598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Novel Attention-Based Generalized Efficient Layer Aggregation Network for Landslide Detection from Satellite Data in the Higher Himalayas, Nepal"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0957-097X","authenticated-orcid":false,"given":"Naveen","family":"Chandra","sequence":"first","affiliation":[{"name":"Geomorphology, Environmental, and Engineering Geology, Wadia Institute of Himalayan Geology, Dehradun 248001, Uttarakhand, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5780-0041","authenticated-orcid":false,"given":"Himadri","family":"Vaidya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun 248002, Uttarakhand, India"},{"name":"Graphic Era Deemed to be University, Dehradun 248002, Uttarakhand, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8526-5734","authenticated-orcid":false,"given":"Suraj","family":"Sawant","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COEP Technological University, Pune 411005, Maharashtra, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6175-6491","authenticated-orcid":false,"given":"Sansar Raj","family":"Meena","sequence":"additional","affiliation":[{"name":"Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padova, 35139 Padova, Italy"},{"name":"Center for Remote Sensing, Department of Earth and Environment, Boston University, Boston, MA 02215, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/s43017-022-00373-x","article-title":"Landslide detection, monitoring and prediction with remote-sensing techniques","volume":"4","author":"Casagli","year":"2023","journal-title":"Nat. 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