{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:42:08Z","timestamp":1775770928931,"version":"3.50.1"},"reference-count":17,"publisher":"Informa UK Limited","issue":"3","funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Science and Technology Program","doi-asserted-by":"publisher","award":["MZGC20230081"],"award-info":[{"award-number":["MZGC20230081"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM","award":["2023-05-14"],"award-info":[{"award-number":["2023-05-14"]}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Parallel, Emergent and Distributed Systems"],"published-print":{"date-parts":[[2025,5,4]]},"DOI":"10.1080\/17445760.2024.2352881","type":"journal-article","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T05:05:19Z","timestamp":1716008719000},"page":"282-295","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":1,"title":["Synergistic fusion network for landslide segmentation: the vital complementarity of geological information to remote sensing imagery"],"prefix":"10.1080","volume":"40","author":[{"given":"Guangle","family":"Yao","sequence":"first","affiliation":[{"name":"Chinese Academy of Surveying and Mapping","place":["Beijing, People's Republic of China"]},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology)","place":["Chengdu, People's Republic of China"]},{"name":"Chengdu University of Technology","place":["Chengdu, People's Republic of China"]}]},{"given":"Chuyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Chengdu University of Technology","place":["Chengdu, People's Republic of China"]}]},{"given":"Haoran","family":"Zhou","sequence":"additional","affiliation":[{"name":"Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","place":["Nanchang, People's Republic of China"]},{"name":"Shenzhen Sensing Data Technology Co., Ltd.","place":["Shenzhen, People's Republic of China"]}]},{"given":"Shaoze","family":"Ye","sequence":"additional","affiliation":[{"name":"Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","place":["Nanchang, People's Republic of China"]},{"name":"Shenzhen Sensing Data Technology Co., Ltd.","place":["Shenzhen, People's Republic of China"]}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping","place":["Beijing, People's Republic of China"]}]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chengdu University of Technology","place":["Chengdu, People's Republic of China"]}]},{"given":"Honghui","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology)","place":["Chengdu, People's Republic of China"]},{"name":"Chengdu University of Technology","place":["Chengdu, People's Republic of China"]}]}],"member":"301","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.5194\/nhess-10-2179-2010"},{"key":"e_1_3_2_3_1","doi-asserted-by":"crossref","unstructured":"Long J Shelhamer E Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Boston (MA) USA; 2015. p. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_4_1","doi-asserted-by":"crossref","unstructured":"Ronneberger O Fischer P Brox T. U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference Munich Germany October 5\u20139 2015 Proceedings Part III 18. Springer; 2015. p. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_5_1","doi-asserted-by":"crossref","unstructured":"Chen L-C Zhu Y Papandreou G et al. Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV); Munich Germany; 2018. p. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2802944"},{"key":"e_1_3_2_7_1","doi-asserted-by":"crossref","unstructured":"Fu J Liu J Tian H et al. Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; Long Beach (CA) USA; 2019. p. 3146\u20133154.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"e_1_3_2_8_1","first-page":"12077","article-title":"Segformer: simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie E","year":"2021","unstructured":"Xie E, Wang W, Yu Z, et\u00a0al. Segformer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst. 2021;34:12077\u201312090.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/Access.6287639"},{"key":"e_1_3_2_10_1","first-page":"920","article-title":"Landslide intelligent recognition based on multi-source data fusion","volume":"45","author":"Xin L-b","year":"2023","unstructured":"Xin L-b, Han L, Li L-z. Landslide intelligent recognition based on multi-source data fusion. J Earth Sci Environ. 2023;45:920\u2013928.","journal-title":"J Earth Sci Environ"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/19475705.2022.2116357"},{"key":"e_1_3_2_12_1","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.11834\/jig.210054","article-title":"Multi-source features adaptation fusion network for semantic segmentation in high-resolution remote sensing images","volume":"27","author":"Zhang WK","year":"2022","unstructured":"Zhang WK, Liu WJ, Sun X, et\u00a0al. Multi-source features adaptation fusion network for semantic segmentation in high-resolution remote sensing images. J Image Graph. 2022;27:2516\u20132526.","journal-title":"J Image Graph"},{"key":"e_1_3_2_13_1","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S et al. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Las Vegas (NV) USA; 2016. p. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_14_1","doi-asserted-by":"crossref","unstructured":"Hu J Shen L Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Salt Lake City (UT) USA; 2018. p. 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_15_1","doi-asserted-by":"crossref","unstructured":"Hou Q Zhou D Feng J. Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2021. p. 13713\u201313722.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"e_1_3_2_16_1","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh O Xu Y Ghamisi P et\u00a0al. Landslide4sense: reference benchmark data and deep learning models for landslide detection; 2022.Available from: arXiv preprint arXiv:2206.00515.","DOI":"10.1109\/TGRS.2022.3215209"},{"key":"e_1_3_2_17_1","doi-asserted-by":"crossref","unstructured":"Jha D Smedsrud PH Riegler MA et\u00a0al. Resunet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM). IEEE; 2019. p. 225\u20132255.","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2983686"}],"container-title":["International Journal of Parallel, Emergent and Distributed Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/17445760.2024.2352881","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T07:01:43Z","timestamp":1745564503000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/17445760.2024.2352881"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,17]]},"references-count":17,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5,4]]}},"alternative-id":["10.1080\/17445760.2024.2352881"],"URL":"https:\/\/doi.org\/10.1080\/17445760.2024.2352881","relation":{},"ISSN":["1744-5760","1744-5779"],"issn-type":[{"value":"1744-5760","type":"print"},{"value":"1744-5779","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,17]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=gpaa20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=gpaa20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2024-04-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}