{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:08:19Z","timestamp":1784203699242,"version":"3.55.0"},"reference-count":139,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51939004"],"award-info":[{"award-number":["51939004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.neucom.2026.134060","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:27:23Z","timestamp":1780356443000},"page":"134060","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["SUGARFuseNet: Diffusion\u2011driven domain adaptation and bimodal bitemporal fusion for advancing global landslide segmentation on novel GBMT\u2011SLID dataset"],"prefix":"10.1016","volume":"696","author":[{"given":"Ghislain Franck","family":"Emani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Weiya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Hadi Shujaie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firdawus","family":"Ssemugga Nattabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanon","family":"Gu\u00e9det Gu\u00e9d\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franck","family":"Ngole Twite","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adangba Raphael","family":"Kouame","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hikma","family":"Ally","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.134060_bib1","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1038\/s41467-020-14445-3","article-title":"Rain and small earthquakes maintain a slow-moving landslide in a persistent critical state","volume":"11","author":"Bontemps","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.neucom.2026.134060_bib2","doi-asserted-by":"crossref","first-page":"469","DOI":"10.5194\/nhess-3-469-2003","article-title":"The impact of landslides in the Umbria region, central Italy","volume":"3","author":"Guzzetti","year":"2003","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.neucom.2026.134060_bib3","doi-asserted-by":"crossref","DOI":"10.1007\/s41748-025-00577-3","article-title":"Advancing Global Landslide Segmentation: A Coupled Multispectral Attention and Data Augmentation Approach Using the Novel MRGSLD Dataset","author":"Emani","year":"2025","journal-title":"Earth Syst. Environ."},{"key":"10.1016\/j.neucom.2026.134060_bib4","first-page":"487","article-title":"Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides","volume":"12","author":"Yang","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.neucom.2026.134060_bib5","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.epsl.2018.11.005","article-title":"Seismic and geological controls on earthquake-induced landslide size","volume":"506","author":"Valagussa","year":"2019","journal-title":"Earth Planet. Sci. Lett."},{"key":"10.1016\/j.neucom.2026.134060_bib6","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1038\/s41598-023-28096-z","article-title":"Comparison of earthquake-induced shallow landslide susceptibility assessment based on two-category LR and KDE-MLR","volume":"13","author":"Fan","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.neucom.2026.134060_bib7","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.rse.2016.10.008","article-title":"Landslide mapping from aerial photographs using change detection-based Markov random field","volume":"187","author":"Li","year":"2016","journal-title":"Remote. Sens. Environ."},{"key":"10.1016\/j.neucom.2026.134060_bib8","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s11069-009-9401-4","article-title":"A global landslide catalog for hazard applications: method, results, and limitations","volume":"52","author":"Kirschbaum","year":"2010","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.neucom.2026.134060_bib9","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.5194\/nhess-15-2111-2015","article-title":"An approach to reduce mapping errors in the production of landslide inventory maps","volume":"15","author":"Santangelo","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.neucom.2026.134060_bib10","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":"10.1016\/j.neucom.2026.134060_bib11","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1007\/s10346-021-01636-2","article-title":"Co-seismic landslide mapping using Sentinel-2 10-m fused NIR narrow, red-edge, and SWIR bands","volume":"18","author":"Lu","year":"2021","journal-title":"Landslides"},{"key":"10.1016\/j.neucom.2026.134060_bib12","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":"2021","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib13","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/LGRS.2020.2979693","article-title":"GAN-Based Siamese Framework for Landslide Inventory Mapping Using Bi-Temporal Optical Remote Sensing Images","volume":"18","author":"Fang","year":"2021","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.neucom.2026.134060_bib14","doi-asserted-by":"crossref","first-page":"4817","DOI":"10.5194\/essd-16-4817-2024","article-title":"A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images","volume":"16","author":"Fang","year":"2024","journal-title":"Earth Syst. Sci. Data"},{"key":"10.1016\/j.neucom.2026.134060_bib15","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":"10.1016\/j.neucom.2026.134060_bib16","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"10.1016\/j.neucom.2026.134060_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.enggeo.2021.106000","article-title":"Landslide mapping using object-based image analysis and open source tools","volume":"282","author":"Amatya","year":"2021","journal-title":"Eng. Geol."},{"key":"10.1016\/j.neucom.2026.134060_bib18","article-title":"The application of ResU-net and OBIA for landslide detection from multi-temporal sentinel-2 images","author":"Ghorbanzadeh","year":"2022","journal-title":"Big Earth Data"},{"key":"10.1016\/j.neucom.2026.134060_bib19","article-title":"Rapid Mapping of Landslides on SAR Data by Attention U-Net","volume":"14","author":"Nava","year":"2022","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib20","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib21","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. Rev. Earth Environ."},{"key":"10.1016\/j.neucom.2026.134060_bib22","article-title":"CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection","volume":"11","author":"Xu","year":"2024","journal-title":"Sci. Data"},{"key":"10.1016\/j.neucom.2026.134060_bib23","article-title":"Remote Sensing of Landslides\u2014A Review","volume":"10","author":"Zhao","year":"2018","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib24","article-title":"Automatic mapping of landslides by the ResU-Net","volume":"12","author":"Qi","year":"2020","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib25","article-title":"An improved segmentation method for automatic mapping of cone karst from remote sensing data based on deeplab V3+ model","volume":"13","author":"Fu","year":"2021","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib26","article-title":"Landslide Segmentation with U-Net: Evaluating Different Sampling Methods and Patch Sizes","volume":"06672","author":"Soares","year":"2020","journal-title":"ArXiv abs\/2007"},{"key":"10.1016\/j.neucom.2026.134060_bib27","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":"10.1016\/j.neucom.2026.134060_bib28","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1007\/s10712-020-09609-1","article-title":"Remote Sensing for Assessing Landslides and Associated Hazards","volume":"41","author":"Lissak","year":"2020","journal-title":"Surv. Geophys."},{"key":"10.1016\/j.neucom.2026.134060_bib29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3215209","article-title":"Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection","volume":"60","author":"Ghorbanzadeh","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib30","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10346-020-01602-4","article-title":"Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach","volume":"18","author":"Meena","year":"2021","journal-title":"Landslides"},{"key":"10.1016\/j.neucom.2026.134060_bib31","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.5194\/essd-15-3283-2023","article-title":"HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery","volume":"15","author":"Meena","year":"2023","journal-title":"Earth Syst. Sci. Data"},{"key":"10.1016\/j.neucom.2026.134060_bib32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2023.01.018","article-title":"Cross-domain landslide mapping from large-scale remote sensing images using prototype-guided domain-aware progressive representation learning","volume":"197","author":"Zhang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib33","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1109\/IGARSS53475.2024.10641224","article-title":"Landslide Mapping from Sentinel-2 Imagery Through Change Detection","author":"Monopoli","year":"2024","journal-title":"IGARSS 2024 - 2024 IEEE Int. Geosci. Remote. Sens. Symp."},{"key":"10.1016\/j.neucom.2026.134060_bib34","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2016.01.003","article-title":"Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method","volume":"175","author":"Li","year":"2016","journal-title":"Remote. Sens. Environ."},{"key":"10.1016\/j.neucom.2026.134060_bib35","article-title":"Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images","volume":"10","author":"Lei","year":"2018","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib36","series-title":"2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC)","first-page":"444","article-title":"Automatic recognition of landslide based on CNN and texture change detection","author":"Ding","year":"2016"},{"key":"10.1016\/j.neucom.2026.134060_bib37","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/LGRS.2018.2889307","article-title":"Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks","volume":"16","author":"Lei","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.neucom.2026.134060_bib38","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib39","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"5967","article-title":"Image-to-Image Translation with Conditional Adversarial Networks","author":"Isola","year":"2017"},{"key":"10.1016\/j.neucom.2026.134060_bib40","series-title":"Computer Vision \u2013 ECCV 2016 Workshops, Springer International Publishing, Cham","first-page":"850","article-title":"Fully-Convolutional Siamese Networks for Object Tracking","author":"Bertinetto","year":"2016"},{"key":"10.1016\/j.neucom.2026.134060_bib41","doi-asserted-by":"crossref","first-page":"2426","DOI":"10.1080\/17538947.2023.2229794","article-title":"DSFA: cross-scene domain style and feature adaptation for landslide detection from high spatial resolution images","volume":"16","author":"Li","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"10.1016\/j.neucom.2026.134060_bib42","first-page":"1","article-title":"Unsupervised Landslide Detection From Multitemporal High-Resolution Images Based on Progressive Label Upgradation and Cross-Temporal Style Adaption","volume":"62","author":"Li","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib43","unstructured":"C. Le, L. Pham, J. Lampert, M. Schl\u00f6gl, A. Schindler, Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network, (2023). \u3008http:\/\/arxiv.org\/abs\/2312.16717\u3009."},{"key":"10.1016\/j.neucom.2026.134060_bib44","article-title":"Landslide Detection Based on ResU-Net with Transformer and CBAM Embedded: Two Examples with Geologically Different Environments","volume":"14","author":"Yang","year":"2022","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib45","article-title":"Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing","volume":"14","author":"Chen","year":"2022","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib46","doi-asserted-by":"crossref","DOI":"10.3390\/s22176412","article-title":"Study on Accuracy Improvement of Slope Failure Region Detection Using Mask R-CNN with Augmentation Method","volume":"22","author":"Kubo","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2026.134060_bib47","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1007\/s10346-024-02274-0","article-title":"Potential of synthetic images in landslide segmentation in data-poor scenario: a framework combining GAN and transformer models","volume":"21","author":"Feng","year":"2024","journal-title":"Landslides"},{"key":"10.1016\/j.neucom.2026.134060_bib48","article-title":"A new integrated approach for landslide data balancing and spatial prediction based on generative adversarial networks (GAN","volume":"13","author":"Al-Najjar","year":"2021","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib49","article-title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","volume":"11096","author":"Brock","year":"2018","journal-title":"ArXiv abs\/1809"},{"key":"10.1016\/j.neucom.2026.134060_bib50","unstructured":"J. Song, C. Meng, S. Ermon, Denoising Diffusion Implicit Models, (2020). \u3008http:\/\/arxiv.org\/abs\/2010.02502\u3009."},{"key":"10.1016\/j.neucom.2026.134060_bib51","unstructured":"J. Ho, A. Jain, P. Abbeel, Denoising Diffusion Probabilistic Models, (2020). \u3008http:\/\/arxiv.org\/abs\/2006.11239\u3009."},{"key":"10.1016\/j.neucom.2026.134060_bib52","series-title":"Proceedings of the 32nd International Conference on Machine Learning","first-page":"2256","article-title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","author":"Sohl-Dickstein","year":"2015"},{"key":"10.1016\/j.neucom.2026.134060_bib53","first-page":"1","article-title":"Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib54","article-title":"GDSNet: A gated dual-stream convolutional neural network for automatic recognition of coseismic landslides","volume":"127","author":"Wang","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.neucom.2026.134060_bib55","doi-asserted-by":"crossref","first-page":"5759","DOI":"10.3390\/rs14225759","article-title":"A network for landslide detection using large-area remote sensing images with multiple spatial resolutions","volume":"14","author":"Yu","year":"2022","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib56","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.neucom.2026.134060_bib57","first-page":"1","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":"10.1016\/j.neucom.2026.134060_bib58","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1007\/s10346-020-01353-2","article-title":"Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks","volume":"17","author":"Ji","year":"2020","journal-title":"Landslides"},{"key":"10.1016\/j.neucom.2026.134060_bib59","doi-asserted-by":"crossref","DOI":"10.3390\/s23094287","article-title":"Enhance the Accuracy of Landslide Detection in UAV Images Using an Improved Mask R-CNN Model: A Case Study of Sanming, China","volume":"23","author":"Yun","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2026.134060_bib60","article-title":"CBAM: Convolutional Block Attention Module","volume":"06521","author":"Woo","year":"2018","journal-title":"CoRR. abs\/1807"},{"key":"10.1016\/j.neucom.2026.134060_bib61","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134060_bib62","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3390\/s23010065","article-title":"Transformer-based weed segmentation for grass management","volume":"23","author":"Jiang","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2026.134060_bib63","first-page":"93","volume":"7","author":"Gibril","year":"2023","journal-title":"Large-Scale date Palm. tree Segm. multiscale uav-Based Aer. Images Using. Deep. Vision. Transform. Drones"},{"key":"10.1016\/j.neucom.2026.134060_bib64","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.3390\/rs14122884","article-title":"Automatic detection of coseismic landslides using a new transformer method","volume":"14","author":"Tang","year":"2022","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib65","first-page":"2681","article-title":"ShapeFormer: A shape-enhanced vision transformer model for optical remote sensing image landslide detection, IEEE J. Sel. Top. Appl. Earth Obs","volume":"16","author":"Lv","year":"2023","journal-title":"Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib66","series-title":"International Conference on Algorithmic Learning Theory, PMLR","first-page":"597","article-title":"On the computational complexity of self-attention","author":"Keles","year":"2023"},{"key":"10.1016\/j.neucom.2026.134060_bib67","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10012","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.neucom.2026.134060_bib68","doi-asserted-by":"crossref","first-page":"3928","DOI":"10.3390\/rs14163928","article-title":"Fast seismic landslide detection based on improved mask R-CNN","volume":"14","author":"Fu","year":"2022","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib69","series-title":"CDCEO@ IJCAI","first-page":"91","article-title":"SwinLS: Adapting Swin Transformer to Landslide Detection","author":"Zhao","year":"2022"},{"key":"10.1016\/j.neucom.2026.134060_bib70","article-title":"Conv-trans dual network for landslide detection of multi-channel optical remote sensing images","volume":"11","author":"Chen","year":"2023","journal-title":"Front. Earth Sci. (Lausanne)"},{"key":"10.1016\/j.neucom.2026.134060_bib71","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.neucom.2026.134060_bib72","doi-asserted-by":"crossref","unstructured":"A. Shaker, M. Maaz, H. Rasheed, S. Khan, M.-H. Yang, F.S. Khan, UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation, (2024). \u3008http:\/\/arxiv.org\/abs\/2212.04497\u3009.","DOI":"10.1109\/TMI.2024.3398728"},{"key":"10.1016\/j.neucom.2026.134060_bib73","article-title":"Deep learning in multimodal remote sensing data fusion: A comprehensive review","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.neucom.2026.134060_bib74","first-page":"1","article-title":"A Multilevel Multimodal Fusion Transformer for Remote Sensing Semantic Segmentation","volume":"62","author":"Ma","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib75","doi-asserted-by":"crossref","first-page":"4340","DOI":"10.1109\/TGRS.2020.3016820","article-title":"More diverse means better: Multimodal deep learning meets remote-sensing imagery classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib76","first-page":"1","article-title":"Convolutional Neural Networks for Multimodal Remote Sensing Data Classification","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib77","first-page":"9927","article-title":"The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection From Multisource Satellite Imagery, IEEE J. Sel. Top. Appl. Earth Obs","volume":"15","author":"Ghorbanzadeh","year":"2022","journal-title":"Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib78","unstructured":"L. Bai, W. Li, Q. Xu, W. Peng, K. Chen, Z. Duan, H. Lu, Multispectral U-Net: A Semantic Segmentation Model Using Multispectral Bands Fusion Mechanism for Landslide Detection, 2022. \u3008http:\/\/ceur-ws.org\u3009."},{"key":"10.1016\/j.neucom.2026.134060_bib79","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote. Sens. Environ."},{"key":"10.1016\/j.neucom.2026.134060_bib80","doi-asserted-by":"crossref","first-page":"7933","DOI":"10.5194\/gmd-15-7933-2022","article-title":"Bayesian atmospheric correction over land: Sentinel-2\/MSI and Landsat 8\/OLI","volume":"15","author":"Yin","year":"2022","journal-title":"Geosci. Model. Dev."},{"key":"10.1016\/j.neucom.2026.134060_bib81","unstructured":"Sentinel-2 PDGS Project Team, Sentinel-2 Calibration and Validation Plan for the Operational Phase, GMES-GSEG-EOPG-PL-10-005 (2014). \u3008www.esa.int\u3009."},{"key":"10.1016\/j.neucom.2026.134060_bib82","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.geomorph.2011.07.021","article-title":"Combining multiple change detection indices for mapping landslides triggered by typhoons","volume":"134","author":"Mondini","year":"2011","journal-title":"Geomorphology"},{"key":"10.1016\/j.neucom.2026.134060_bib83","doi-asserted-by":"crossref","first-page":"3572","DOI":"10.1080\/01431161.2023.2224096","article-title":"Combination of optical images and SAR images for detecting landslide scars, using a classification and regression tree","volume":"44","author":"Phakdimek","year":"2023","journal-title":"Int. J. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib84","article-title":"Barest Pixel Composite for Agricultural Areas Using Landsat Time Series","volume":"9","author":"Diek","year":"2017","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib85","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1007\/s10661-022-10514-w","article-title":"Spatio-temporal landslide inventory and susceptibility assessment using Sentinel-2 in the Himalayan mountainous region of Pakistan","volume":"194","author":"Bacha","year":"2022","journal-title":"Environ. Monit. Assess."},{"key":"10.1016\/j.neucom.2026.134060_bib86","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2022.112990","article-title":"Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2","volume":"274","author":"Skakun","year":"2022","journal-title":"Remote. Sens. Environ."},{"key":"10.1016\/j.neucom.2026.134060_bib87","doi-asserted-by":"crossref","unstructured":"AIRBUS, Copernicus DEM Copernicus Digital Elevation Model Product Handbook, 2022. https:\/\/doi.org\/https:\/\/doi.org\/10.5270\/ESA-c5d3d65.","DOI":"10.5270\/ESA-c5d3d65"},{"key":"10.1016\/j.neucom.2026.134060_bib88","article-title":"A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping","volume":"108","author":"Lv","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.neucom.2026.134060_bib89","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40562-019-0140-4","article-title":"Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia","volume":"6","author":"Silalahi","year":"2019","journal-title":"Geosci. Lett."},{"key":"10.1016\/j.neucom.2026.134060_bib90","article-title":"Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping","volume":"13","author":"Yang","year":"2021","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib91","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2020.143785","article-title":"Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition","volume":"757","author":"Kopeck\u00fd","year":"2021","journal-title":"Sci. Total. Environ."},{"key":"10.1016\/j.neucom.2026.134060_bib92","series-title":"Image analysis, classification and change detection in remote sensing: with algorithms for Python","author":"Canty","year":"2019"},{"key":"10.1016\/j.neucom.2026.134060_bib93","article-title":"A Deep Learning Semantic Segmentation Method for Landslide Scene Based on Transformer Architecture","volume":"14","author":"Wang","year":"2022","journal-title":"Sustain. (Switz. )"},{"key":"10.1016\/j.neucom.2026.134060_bib94","series-title":"Palette: Image-to-image diffusion models","first-page":"1","article-title":"Palette: Image-to-image diffusion models","author":"Saharia","year":"2022"},{"key":"10.1016\/j.neucom.2026.134060_bib95","unstructured":"M. Xia, Y. Zhou, R. Yi, Y.-J. Liu, W. Wang, A. Diffusion Model Translator for Efficient Image-to-Image Translation, (2025). \u3008http:\/\/arxiv.org\/abs\/2502.00307\u3009."},{"key":"10.1016\/j.neucom.2026.134060_bib96","doi-asserted-by":"crossref","DOI":"10.1109\/LGRS.2023.3316282","article-title":"High-Confidence Sample Augmentation Based on Label-Guided Denoising Diffusion Probabilistic Model for Active Deception Jamming Recognition","volume":"20","author":"Wu","year":"2023","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.neucom.2026.134060_bib97","series-title":"Adv. Neural Inf. Process","first-page":"8780","article-title":"Diffusion Models Beat GANs on Image Synthesis","author":"Dhariwal","year":"2021"},{"key":"10.1016\/j.neucom.2026.134060_bib98","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1109\/JSTARS.2022.3157648","article-title":"A Siamese Network Based U-Net for Change Detection in High Resolution Remote Sensing Images","volume":"15","author":"Chen","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib99","doi-asserted-by":"crossref","DOI":"10.1080\/10106049.2024.2322080","article-title":"A dual-difference change detection network for detecting building changes on high-resolution remote sensing images","volume":"39","author":"Xu","year":"2024","journal-title":"Geocarto Int."},{"key":"10.1016\/j.neucom.2026.134060_bib100","series-title":"Computer Vision \u2013 ECCV 2018","first-page":"833","article-title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.neucom.2026.134060_bib101","article-title":"SC-CAN: Spectral Convolution and Channel Attention Network for Wheat Stress Classification","volume":"14","author":"Khotimah","year":"2022","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib102","first-page":"2020","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale, ArXiv Preprint","volume":"11929","author":"Dosovitskiy","year":"2010","journal-title":"ArXiv"},{"key":"10.1016\/j.neucom.2026.134060_bib103","doi-asserted-by":"crossref","first-page":"7132","DOI":"10.1109\/CVPR.2018.00745","article-title":"Squeeze-and-Excitation Networks","author":"Hu","year":"2018","journal-title":"2018 IEEE\/CVF Conf. Comput. Vision. Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.134060_bib104","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2024.120773","article-title":"STIRUnet: SwinTransformer and inverted residual convolution embedding in unet for Sea\u2013Land segmentation","volume":"357","author":"Tong","year":"2024","journal-title":"J. Environ. Manag."},{"key":"10.1016\/j.neucom.2026.134060_bib105","series-title":"Machine Learning in Medical Imaging","first-page":"379","article-title":"Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks","author":"Salehi","year":"2017"},{"key":"10.1016\/j.neucom.2026.134060_bib106","doi-asserted-by":"crossref","first-page":"4413","DOI":"10.1109\/CVPR.2018.00464","article-title":"The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks","author":"Berman","year":"2018","journal-title":"2018 IEEE\/CVF Conf. Comput. Vision. Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.134060_bib107","first-page":"04306","article-title":"Transunet: Transformers make strong encoders for medical image segmentation, ArXiv Preprint","volume":"2102","author":"Chen","year":"2021","journal-title":"ArXiv"},{"key":"10.1016\/j.neucom.2026.134060_bib108","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134060_bib109","first-page":"3463","article-title":"A crossmodal multiscale fusion network for semantic segmentation of remote sensing data, IEEE J. Sel. Top. Appl","volume":"15","author":"Ma","year":"2022","journal-title":"Earth Obs. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib110","series-title":"IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium","first-page":"207","article-title":"A transformer-based siamese network for change detection","author":"Bandara","year":"2022"},{"key":"10.1016\/j.neucom.2026.134060_bib111","article-title":"A Siamese Swin-Unet for image change detection","volume":"14","author":"Tang","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.neucom.2026.134060_bib112","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1002\/gdj3.145","article-title":"Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method","volume":"9","author":"Amatya","year":"2022","journal-title":"Geosci. Data J."},{"key":"10.1016\/j.neucom.2026.134060_bib113","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2018.03.016","article-title":"How far are we from the use of satellite rainfall products in landslide forecasting?","volume":"210","author":"Brunetti","year":"2018","journal-title":"Remote. Sens. Environ."},{"key":"10.1016\/j.neucom.2026.134060_bib114","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1109\/CVPR.2019.00326","article-title":"Dual Attention Network for Scene Segmentation","author":"Fu","year":"2019","journal-title":"2019 IEEE\/CVF Conf. Comput. Vision. Pattern Recognit. (CVPR)"},{"key":"10.1016\/j.neucom.2026.134060_bib115","doi-asserted-by":"crossref","DOI":"10.1016\/j.jss.2022.111359","article-title":"Data management for production quality deep learning models: Challenges and solutions","volume":"191","author":"Munappy","year":"2022","journal-title":"J. Syst. Softw."},{"key":"10.1016\/j.neucom.2026.134060_bib116","doi-asserted-by":"crossref","first-page":"11561","DOI":"10.1109\/JSTARS.2025.3559884","article-title":"A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model","volume":"18","author":"Hou","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib117","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1007\/s10346-022-01861-3","article-title":"Landslide detection in the Himalayas using machine learning algorithms and U-Net","volume":"19","author":"Meena","year":"2022","journal-title":"Landslides"},{"key":"10.1016\/j.neucom.2026.134060_bib118","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.1109\/JSTARS.2022.3177025","article-title":"Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates","volume":"15","author":"Nagendra","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"10.1016\/j.neucom.2026.134060_bib119","article-title":"Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review","volume":"105","author":"Wambugu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.neucom.2026.134060_bib120","article-title":"Landslides triggered by the December 24","author":"Garc\u00eda-Delgado","year":"2021","journal-title":"2019 Mesetas (Meta Colomb. ) Earthq."},{"key":"10.1016\/j.neucom.2026.134060_bib121","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.geomorph.2015.03.016","article-title":"Spatial and temporal analysis of a global landslide catalog","volume":"249","author":"Kirschbaum","year":"2015","journal-title":"Geomorphology"},{"key":"10.1016\/j.neucom.2026.134060_bib122","doi-asserted-by":"crossref","unstructured":"S. Raj Meena, L. Nava, K. Bhuyan, S. Puliero, L. Pedrosa, H. Cristina Dias, M. Floris, F. Catani, HR-GLDD: A globally distributed dataset using generalized DL for rapid landslide 1 mapping on HR satellite imagery 2, (n.d.). https:\/\/doi.org\/10.5194\/essd-2022-350.","DOI":"10.5194\/essd-2022-350"},{"key":"10.1016\/j.neucom.2026.134060_bib123","unstructured":"J.C.J. van Westen and Zhang, Landslides and floods triggered by Hurricane Maria (18 September, 2017) in Dominica, (n.d.). \u3008http:\/\/www.unitar.org\/unosat\/node\/44\/2762\u3009 (accessed January 11, 2024)."},{"key":"10.1016\/j.neucom.2026.134060_bib124","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-023-02336-3","article-title":"Inventory of landslides triggered by an extreme rainfall event in Marche-Umbria, Italy, on 15 September 2022","volume":"10","author":"Santangelo","year":"2023","journal-title":"Sci. Data"},{"key":"10.1016\/j.neucom.2026.134060_bib125","article-title":"The unsuPervised shAllow laNdslide rapiD mApping: PANDA method applied to severe rainfalls in northeastern appenine (Italy","volume":"129","author":"Notti","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.neucom.2026.134060_bib126","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.5194\/nhess-22-3679-2022","article-title":"Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments","volume":"22","author":"Deijns","year":"2022","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.neucom.2026.134060_bib127","doi-asserted-by":"crossref","first-page":"445","DOI":"10.5194\/esurf-9-445-2021","article-title":"Interactions between deforestation, landscape rejuvenation, and shallow landslides in the North Tanganyika\u2013Kivu rift region, Africa","volume":"9","author":"Depicker","year":"2021","journal-title":"Earth Surf. Dyn."},{"key":"10.1016\/j.neucom.2026.134060_bib128","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.5194\/nhess-22-1129-2022","article-title":"Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories","volume":"22","author":"Emberson","year":"2022","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.neucom.2026.134060_bib129","article-title":"Data-Driven Landslide Nowcasting at the Global Scale","volume":"9","author":"Stanley","year":"2021","journal-title":"Front. Earth Sci. (Lausanne)"},{"key":"10.1016\/j.neucom.2026.134060_bib130","article-title":"Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal","volume":"11","author":"Amatya","year":"2019","journal-title":"Remote. Sens. (Basel)"},{"key":"10.1016\/j.neucom.2026.134060_bib131","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.1007\/s10064-021-02238-x","article-title":"Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides","volume":"80","author":"Tanya\u015f","year":"2021","journal-title":"Bull. Eng. Geol. Environ."},{"key":"10.1016\/j.neucom.2026.134060_bib132","doi-asserted-by":"crossref","DOI":"10.1016\/j.eqrea.2022.100181","article-title":"An open-accessed inventory of landslides triggered by the MS 6.8 Luding earthquake, China on September 5, 2022","volume":"3","author":"Huang","year":"2023","journal-title":"Earthq. Res. Adv."},{"key":"10.1016\/j.neucom.2026.134060_bib133","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.nhres.2022.09.001","article-title":"Two public inventories of landslides induced by the 10 June 2022 Maerkang Earthquake swarm, China and ancient landslides in the affected area","volume":"2","author":"Shao","year":"2022","journal-title":"Nat. Hazards Res."},{"key":"10.1016\/j.neucom.2026.134060_bib134","author":"H\u00f6lbling","year":"2024","journal-title":"Butan Land. Land. -dammed lake Outl. Based Landsat Time Ser. Respect. typhoons"},{"key":"10.1016\/j.neucom.2026.134060_bib135","unstructured":"The Association of Japanese Geographer, The 2018 July Heavy rain in West Japan, (n.d.). \u3008http:\/\/ajg-disaster.blogspot.com\/2018\/07\/\u3009 3077.html, last access: 1 November 2019. (accessed November 1, 2019)."},{"key":"10.1016\/j.neucom.2026.134060_bib136","unstructured":"R., K.D, A.P., T.H, and M.O. Emberson, Topographic characteristics of rainfall triggered landslides from a newly compiled set of inventories, EGU General Assembly 2021 (2021)."},{"key":"10.1016\/j.neucom.2026.134060_bib137","article-title":"Satellite Image Maps and GIS File of the Masara, Maco","author":"Santillan","year":"2024","journal-title":"Davao De. Oro Land."},{"key":"10.1016\/j.neucom.2026.134060_bib138","doi-asserted-by":"crossref","DOI":"10.1016\/j.enggeo.2021.106504","article-title":"The world\u2019s second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake","volume":"297","author":"Tanya\u015f","year":"2022","journal-title":"Eng. Geol."},{"key":"10.1016\/j.neucom.2026.134060_bib139","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1007\/s10346-022-01869-9","article-title":"An open dataset for landslides triggered by the 2016 Mw 7.8 Kaik\u014dura earthquake, New Zealand","volume":"19","author":"Tanya\u015f","year":"2022","journal-title":"Landslides"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122601458X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122601458X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:49:27Z","timestamp":1784202567000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S092523122601458X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":139,"alternative-id":["S092523122601458X"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134060","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SUGARFuseNet: Diffusion\u2011driven domain adaptation and bimodal bitemporal fusion for advancing global landslide segmentation on novel GBMT\u2011SLID dataset","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134060","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"134060"}}