{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T06:54:41Z","timestamp":1768632881853,"version":"3.49.0"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024T170759"],"award-info":[{"award-number":["2024T170759"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001114","name":"Endocrine Society of Australia","doi-asserted-by":"publisher","award":["95355"],"award-info":[{"award-number":["95355"]}],"id":[{"id":"10.13039\/501100001114","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3008402"],"award-info":[{"award-number":["2023YFC3008402"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42304029"],"award-info":[{"award-number":["42304029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000266","name":"National Geospatial-Intelligence Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Applied Earth Observation and Geoinformation"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.jag.2025.104882","type":"journal-article","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T02:35:48Z","timestamp":1759545348000},"page":"104882","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A lightweight Context-aware adaptive fusion network for automatic identification of active landslides"],"prefix":"10.1016","volume":"144","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-0961","authenticated-orcid":false,"given":"Xingmin","family":"Cai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9181-9529","authenticated-orcid":false,"given":"Chuang","family":"Song","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-7449","authenticated-orcid":false,"given":"Zhenhong","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8449-0427","authenticated-orcid":false,"given":"Yi","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2461-5256","authenticated-orcid":false,"given":"Bo","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1375-4085","authenticated-orcid":false,"given":"Jiantao","family":"Du","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9675-8814","authenticated-orcid":false,"given":"Chen","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Wu","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Jianbing","family":"Peng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.jag.2025.104882_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2019.04.032","article-title":"A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets","volume":"230","author":"Anantrasirichai","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.jag.2025.104882_b0010","doi-asserted-by":"crossref","first-page":"2940","DOI":"10.1109\/TGRS.2020.3018315","article-title":"Detecting Ground Deformation in the Built Environment Using Sparse Satellite InSAR Data with a Convolutional Neural Network","volume":"59","author":"Anantrasirichai","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2020.111983","article-title":"InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal","volume":"249","author":"Bekaert","year":"2020","journal-title":"Remote Sensing of Environment"},{"key":"10.1016\/j.jag.2025.104882_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.jag.2023.103516","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":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"10.1016\/j.jag.2025.104882_b0025","article-title":"Automatic detection of active geohazards with millimeter-to-meter-scale deformation and quantitative analysis of factors influencing spatial distribution: A case study in the Hexi corridor, China","volume":"131","author":"Chen","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.jag.2025.104882_b0030","doi-asserted-by":"crossref","DOI":"10.1080\/17538947.2024.2393261","article-title":"Automatic detection of earthquake triggered landslides using Sentinel-1 SAR imagery based on deep learning","volume":"17","author":"Chen","year":"2024","journal-title":"Int. J. Digit. Earth."},{"key":"10.1016\/j.jag.2025.104882_b0035","series-title":"Proceedings of the European conference on computer vision (ECCV)","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.jag.2025.104882_b0040","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.3390\/rs14081848","article-title":"DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai\u2013Tibet Plateau","volume":"14","author":"Chen","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0045","article-title":"An Embedding Swin Transformer Model for Automatic Slow-moving Landslides Detection based on InSAR Products","volume":"1\u20131","author":"Chen","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.enggeo.2024.107748","article-title":"Characterizing the evolution of the Daguangbao landslide nearly 15 years after the 2008 Wenchuan earthquake by InSAR observations","volume":"342","author":"Chen","year":"2024","journal-title":"Eng. Geol."},{"key":"10.1016\/j.jag.2025.104882_b0055","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1109\/36.673674","article-title":"A novel phase unwrapping method based on network programming","volume":"36","author":"Costantini","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0060","unstructured":"Cruden D., Varnes D., 1996. Landslide types and processes. In: Turner AK, Schuster RL (eds) Landslides investigation and mitigation. Transportation research board, US National Research Council. Special Report 247, Washington, DC, Chapter 3, pp. 36\u201375."},{"key":"10.1016\/j.jag.2025.104882_b0065","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0070","doi-asserted-by":"crossref","first-page":"3819","DOI":"10.1109\/JSTARS.2023.3263003","article-title":"InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River","volume":"16","author":"Du","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0075","doi-asserted-by":"crossref","DOI":"10.3389\/fenvs.2022.963322","article-title":"Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network","volume":"10","author":"Fu","year":"2022","journal-title":"Front. Environ. Sci."},{"key":"10.1016\/j.jag.2025.104882_b0080","first-page":"1140","article-title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","volume":"35","author":"Guo","year":"2022","journal-title":"Advances in neural inf. process. systems"},{"key":"10.1016\/j.jag.2025.104882_b0085","first-page":"1","article-title":"A Mask R-CNN Network for Wide-Area Mining Subsidence Automatic Detection with InSAR Observations","volume":"62","author":"He","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0090","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10346-013-0436-y","article-title":"The Varnes classification of landslide types, an update","volume":"11","author":"Hungr","year":"2014","journal-title":"Landslides"},{"key":"10.1016\/j.jag.2025.104882_b0095","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.isprsjprs.2024.10.006","article-title":"PolyR-CNN: R-CNN for end-to-end polygonal building outline extraction","volume":"218","author":"Jiao","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2023.113620","article-title":"Inferring slip-surface geometry and volume of creeping landslides based on InSAR: A case study in Jinsha River basin","volume":"294","author":"Kang","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.jag.2025.104882_b0105","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/s43017-020-0072-8","article-title":"Life and death of slow-moving landslides","volume":"1","author":"Lacroix","year":"2020","journal-title":"Nat. Rev. Earth Environ."},{"key":"10.1016\/j.jag.2025.104882_b0110","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s10346-021-01789-0","article-title":"Detection and segmentation of loess landslides via satellite images: a two-phase framework","volume":"19","author":"Li","year":"2022","journal-title":"Landslides"},{"key":"10.1016\/j.jag.2025.104882_b0115","doi-asserted-by":"crossref","first-page":"992","DOI":"10.3390\/rs16060992","article-title":"Deep Learning-Based Landslide Recognition Incorporating Deformation Characteristics","volume":"16","author":"Li","year":"2024","journal-title":"Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0120","doi-asserted-by":"crossref","first-page":"5262","DOI":"10.3390\/rs15215262","article-title":"Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model","volume":"15","author":"Liang","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3334492","article-title":"Feature-Fusion Segmentation Network for Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data","volume":"61","author":"Liu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.enggeo.2021.106033","article-title":"Integration of Sentinel-1 and ALOS\/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China","volume":"284","author":"Liu","year":"2021","journal-title":"Eng. Geol."},{"key":"10.1016\/j.jag.2025.104882_b0135","article-title":"New Insights of the Conjugate Seismogenic Structure in the Northernest Longitudinal Valley Revealed by the 2024 Hualien (Taiwan) Earthquake from Geodetic and Seismic Observations","author":"Liu","year":"2024","journal-title":"ESS Open Archive."},{"key":"10.1016\/j.jag.2025.104882_b0145","doi-asserted-by":"crossref","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-Science Reviews"},{"key":"10.1016\/j.jag.2025.104882_b0150","doi-asserted-by":"crossref","first-page":"584","DOI":"10.3390\/ijgi9100584","article-title":"ADAtools: Automatic Detection and Classification of Active Deformation Areas from PSI Displacement Maps","volume":"9","author":"Navarro","year":"2020","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"10.1016\/j.jag.2025.104882_b0155","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.enggeo.2014.08.015","article-title":"Heavy rainfall triggered loess\u2013mudstone landslide and subsequent debris flow in Tianshui, China","volume":"186","author":"Peng","year":"2015","journal-title":"Eng. Geol."},{"key":"10.1016\/j.jag.2025.104882_b0160","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":"10.1016\/j.jag.2025.104882_b0165","doi-asserted-by":"crossref","first-page":"6480","DOI":"10.1038\/s41467-021-26254-3","article-title":"Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning","volume":"12","author":"Rouet-Leduc","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jag.2025.104882_b0170","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1007\/s11069-023-06067-x","article-title":"Megalandslides and deglaciation: modelling of two case studies in the Central Andes","volume":"118","author":"Sep\u00falveda","year":"2023","journal-title":"Nat. Hazards."},{"key":"10.1016\/j.jag.2025.104882_b0180","doi-asserted-by":"crossref","first-page":"7278","DOI":"10.1038\/s41467-022-35035-5","article-title":"Triggering and recovery of earthquake accelerated landslides in Central Italy revealed by satellite radar observations","volume":"13","author":"Song","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jag.2025.104882_b0185","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.3390\/rs11212575","article-title":"Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas","volume":"11","author":"Tavakkoli Piralilou","year":"2019","journal-title":"Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0190","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.isprsjprs.2022.06.008","article-title":"UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery","volume":"190","author":"Wang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0195","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"11534","article-title":"ECA-Net: Efficient channel attention for deep convolutional neural networks","author":"Wang","year":"2020"},{"key":"10.1016\/j.jag.2025.104882_b0200","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/B978-0-12-818464-6.00006-8","article-title":"Remote sensing of landslide motion with emphasis on satellite multi-temporal interferometry applications: An overview","author":"Wasowski","year":"2022","journal-title":"Landslide hazards, risks, and disasters"},{"key":"10.1016\/j.jag.2025.104882_b0205","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.enggeo.2014.03.003","article-title":"Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives","volume":"174","author":"Wasowski","year":"2014","journal-title":"Eng. Geol."},{"key":"10.1016\/j.jag.2025.104882_b0210","article-title":"Landslide mapping based on a hybrid CNN-transformer network and deep transfer learning using remote sensing images with topographic and spectral features","volume":"126","author":"Wu","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.jag.2025.104882_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.enggeo.2023.107156","article-title":"Remote sensing for landslide investigations: A progress report from China","volume":"321","author":"Xu","year":"2023","journal-title":"Eng. Geol."},{"key":"10.1016\/j.jag.2025.104882_b0220","doi-asserted-by":"crossref","first-page":"3566","DOI":"10.3390\/rs13183566","article-title":"Landslide Detection in the Linzhi\u2013Ya\u2019an Section along the Sichuan\u2013Tibet Railway Based on InSAR and Hot Spot Analysis Methods","volume":"13","author":"Zhang","year":"2021","journal-title":"Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0225","first-page":"1","article-title":"Reduction of Atmospheric Effects on InSAR Observations Through Incorporation of GACOS and PCA Into Small Baseline Subset InSAR","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.jag.2025.104882_b0230","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"6230","article-title":"Pyramid Scene Parsing Network","author":"Zhao","year":"2017"},{"issue":"6","key":"10.1016\/j.jag.2025.104882_b0235","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE transactions on medical imaging"}],"container-title":["International Journal of Applied Earth Observation and Geoinformation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1569843225005291?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1569843225005291?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:26:56Z","timestamp":1768591616000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1569843225005291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":45,"alternative-id":["S1569843225005291"],"URL":"https:\/\/doi.org\/10.1016\/j.jag.2025.104882","relation":{},"ISSN":["1569-8432"],"issn-type":[{"value":"1569-8432","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A lightweight Context-aware adaptive fusion network for automatic identification of active landslides","name":"articletitle","label":"Article Title"},{"value":"International Journal of Applied Earth Observation and Geoinformation","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jag.2025.104882","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104882"}}