{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:45:07Z","timestamp":1781883907808,"version":"3.54.5"},"reference-count":36,"publisher":"Informa UK Limited","issue":"1","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A2013"],"award-info":[{"award-number":["U21A2013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42311530065"],"award-info":[{"award-number":["42311530065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Operation and Maintenance Project of Big Earth Data Center of the Chinese Academy of Sciences","award":["CAS-WX2022SDC-XK13"],"award-info":[{"award-number":["CAS-WX2022SDC-XK13"]}]},{"name":"Joint HKU-CAS Laboratory for iEarth","award":["313GJHZ2022074MI"],"award-info":[{"award-number":["313GJHZ2022074MI"]}]},{"name":"Royal Society International Exchanges Project","award":["IEC\/NSFC\/223528"],"award-info":[{"award-number":["IEC\/NSFC\/223528"]}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Digital Earth"],"published-print":{"date-parts":[[2025,8,25]]},"DOI":"10.1080\/17538947.2025.2542913","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T23:50:53Z","timestamp":1755215453000},"update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":1,"title":["A segment anything model-based geological remote sensing interpretation method with a distributed data-parallel deep learning framework"],"prefix":"10.1080","volume":"18","author":[{"given":"Xiaohui","family":"Huang","sequence":"first","affiliation":[{"name":"China University of Geosciences","place":["Wuhan, People's Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ao","family":"Long","sequence":"additional","affiliation":[{"name":"China University of Geosciences","place":["Wuhan, People's Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Han","sequence":"additional","affiliation":[{"name":"China University of Geosciences","place":["Wuhan, People's Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunliang","family":"Chen","sequence":"additional","affiliation":[{"name":"China University of Geosciences","place":["Wuhan, People's Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geyong","family":"Min","sequence":"additional","affiliation":[{"name":"University of Exeter","place":["Exeter, UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongmei","family":"Yan","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals","place":["Beijing, People's Republic of China"]},{"name":"Chinese Academy of Sciences","place":["Beijing, People's Republic of China"]},{"name":"University of Chinese Academy of Sciences","place":["Beijing, People's Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"301","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2021.102549"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106014"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2025.04.015"},{"key":"e_1_3_3_5_1","unstructured":"Chen L. G. Papandreou F. Schroff and H. Adam. 2017. \u201cRethinking Atrous Convolution for Semantic Image Segmentation.\u201d CoRR abs\/1706.05587."},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362707"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rsase.2024.101218"},{"key":"e_1_3_3_8_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3183080","article-title":"Geological Remote Sensing Interpretation Using Deep Learning Feature and an Adaptive Multisource Data Fusion Network","volume":"60","author":"Han W.","year":"2022","unstructured":"Han, W., J. Li, S. Wang, X. Zhang, Y. Dong, R. Fan, X. Zhang, and L. Wang. 2022. \u201cGeological Remote Sensing Interpretation Using Deep Learning Feature and an Adaptive Multisource Data Fusion Network.\u201d IEEE Transactions on Geoscience and Remote Sensing60:1\u201314.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.59717\/j.xinn-geo.2024.100122"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2023.05.032"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.36"},{"key":"e_1_3_3_12_1","unstructured":"Hong D. B. Zhang X. Li Y. Li C. Li J. Yao N. Yokoya et al. 2023. SpectralGPT: Spectral Foundation Model. CoRR abs\/2311.07113."},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.4609443"},{"key":"e_1_3_3_14_1","unstructured":"Ke L. M. Ye M. Danelljan Y. Liu Y. Tai C. Tang and F. Yu. 2023 December 10\u201316. \u201cSegment Anything in High Quality.\u201d In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023 Neurips 2023 edited by A. Oh T. Naumann A. Globerson K. Saenko M. Hardtand and S. Levine. New Orleans LA USA."},{"key":"e_1_3_3_15_1","unstructured":"Kingma D. P. and J. Ba. 2015 May 7\u20139. \u201cAdam: A Method for Stochastic Optimization.\u201d In 3rd International Conference on Learning Representations ICLR 2015 Conference Track Proceedings edited by Y. Bengio and Y. LeCun. San Diego CA USA. Openreview.net."},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2024.3490534"},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3243954"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3339291"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2025.3531879"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs13163117"},{"issue":"1","key":"e_1_3_3_22_1","first-page":"1235","article-title":"Mllib: Machine Learning in Apache Spark","volume":"17","author":"Meng X.","year":"2016","unstructured":"Meng, X., J. Bradley, B. Yavuz, E. Sparks, S. Venkataraman, D. Liu, J. Freeman, et al. 2016. \u201cMllib: Machine Learning in Apache Spark.\u201d The Journal of Machine Learning Research 17 (1): 1235\u20131241.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2024.104139"},{"key":"e_1_3_3_24_1","first-page":"561","volume-title":"13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018","author":"Moritz P.","year":"2018","unstructured":"Moritz, P., R. Nishihara, S. Wang, A. Tumanov, R. Liaw, E. Liang, M. Elibol, et al. 2018, October 8-10. \u201cRay: A Distributed Framework for Emerging AI Applications.\u201d In 13th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2018, edited by A. C. Arpaci-Dusseau and G. Voelker, 561\u2013577. Carlsbad, CA, USA: USENIX Association."},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3203606"},{"key":"e_1_3_3_26_1","unstructured":"Sergeev A. and M. D. Balso. 2018. \u201cHorovod: Fast and Easy Distributed Deep Learning in TensorFlow.\u201d CoRR abs\/1802.05799."},{"issue":"2","key":"e_1_3_3_27_1","first-page":"495","article-title":"GradientFlow: Optimizing Network Performance for Large-Scale Distributed DNN Training","volume":"8","author":"Sun P.","year":"2022","unstructured":"Sun, P., Y. Wen, R. Han, W. Feng, and S. Yan. 2022. \u201cGradientFlow: Optimizing Network Performance for Large-Scale Distributed DNN Training.\u201d IEEE Transactions on Big Data 8 (2): 495\u2013507.","journal-title":"IEEE Transactions on Big Data"},{"key":"e_1_3_3_28_1","unstructured":"Tripathy P. K. Baylis K. Wu J. Watson and R. Jiang. 2024. \u201cInvestigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels.\u201d CoRR abs\/2407.01846."},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/79173.79181"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2023.103536"},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2024.104258"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2021.102409"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2023.100519"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2023.103429"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2022.102681"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2024.103976"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3327774"}],"container-title":["International Journal of Digital Earth"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/17538947.2025.2542913","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T11:27:03Z","timestamp":1756121223000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/17538947.2025.2542913"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,14]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,8,25]]}},"alternative-id":["10.1080\/17538947.2025.2542913"],"URL":"https:\/\/doi.org\/10.1080\/17538947.2025.2542913","relation":{},"ISSN":["1753-8947","1753-8955"],"issn-type":[{"value":"1753-8947","type":"print"},{"value":"1753-8955","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,14]]},"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=tjde20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tjde20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2025-01-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"2542913"}}