{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T02:20:33Z","timestamp":1769739633456,"version":"3.49.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306211"],"award-info":[{"award-number":["62306211"]}],"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":["62271345"],"award-info":[{"award-number":["62271345"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s12145-024-01381-9","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T12:01:54Z","timestamp":1720180914000},"page":"4361-4375","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["From land to ocean: bathymetric terrain reconstruction via conditional generative adversarial network"],"prefix":"10.1007","volume":"17","author":[{"given":"Liwen","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jiabao","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Ziqiang","family":"Huo","sequence":"additional","affiliation":[]},{"given":"Zhengjian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Xi","sequence":"additional","affiliation":[]},{"given":"Jiachen","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"1381_CR1","doi-asserted-by":"crossref","unstructured":"Alzahem A, Boulila W, Koubaa A et\u00a0al (2023) Improving satellite image classification accuracy using gan-based data augmentation and vision transformers. Earth Sci Inf 16(4):4169\u20134186","DOI":"10.1007\/s12145-023-01153-x"},{"issue":"1","key":"1381_CR2","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s12145-022-00930-4","volume":"16","author":"S Bibi","year":"2023","unstructured":"Bibi S, Shafique M, Ali N et al (2023) Estimation of glacier mass balance using remote sensing and gis technology in the hindu kush region of northern pakistan. Earth Sci Inf 16(1):193\u2013203","journal-title":"Earth Sci Inf"},{"issue":"6","key":"1381_CR3","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.cageo.2009.12.001","volume":"36","author":"C Chen","year":"2010","unstructured":"Chen C, Yue T (2010) A method of dem construction and related error analysis. Computers & Geosciences 36(6):717\u2013725","journal-title":"Computers & Geosciences"},{"key":"1381_CR4","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.apnum.2016.11.003","volume":"113","author":"A Crivellaro","year":"2017","unstructured":"Crivellaro A, Perotto S, Zonca S (2017) Reconstruction of 3d scattered data via radial basis functions by efficient and robust techniques. Appl Numer Math 113:93\u2013108","journal-title":"Appl Numer Math"},{"key":"1381_CR5","doi-asserted-by":"crossref","unstructured":"Dong G, Smith WA, Huang W et al (2019) Filling voids in elevation models using a shadow-constrained convolutional neural network. IEEE Geosci Remote Sens Lett 17(4):592\u2013596","DOI":"10.1109\/LGRS.2019.2926530"},{"key":"1381_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2019.111602","volume":"237","author":"G Dong","year":"2020","unstructured":"Dong G, Huang W, Smith WA et al (2020) A shadow constrained conditional generative adversarial net for srtm data restoration. Remote Sens Environ 237:111602","journal-title":"Remote Sens Environ"},{"key":"1381_CR7","doi-asserted-by":"crossref","unstructured":"Dong G, Chen F, Ren P (2018) Filling srtm void data via conditional adversarial networks. In: IGARSS 2018-2018 IEEE international geoscience and remote sensing symposium, IEEE, pp 7441\u20137443","DOI":"10.1109\/IGARSS.2018.8518992"},{"issue":"10","key":"1381_CR8","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1109\/LGRS.2019.2902222","volume":"16","author":"K Gavriil","year":"2019","unstructured":"Gavriil K, Muntingh G, Barrowclough OJ (2019) Void filling of digital elevation models with deep generative models. IEEE Geosci Remote Sens Lett 16(10):1645\u20131649","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1381_CR9","doi-asserted-by":"crossref","unstructured":"Ghezelbash R, Maghsoudi A, Carranza EJM (2019) Performance evaluation of rbf-and svm-based machine learning algorithms for predictive mineral prospectivity modeling: integration of sa multifractal model and mineralization controls. Earth Sci Inf 12(3):277\u2013293","DOI":"10.1007\/s12145-018-00377-6"},{"key":"1381_CR10","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M et\u00a0al (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27"},{"issue":"3","key":"1381_CR11","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/JAS.2023.123117","volume":"10","author":"J He","year":"2023","unstructured":"He J, Wen J, Xiao S et al (2023) Multi-auv inspection for process monitoring of underwater oil transportation. IEEE\/CAA Journal of Automatica Sinica 10(3):828\u2013830","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"1381_CR12","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et\u00a0al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1381_CR13","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L et\u00a0al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1381_CR14","unstructured":"Jolicoeur-Martineau A (2018) The relativistic discriminator: a key element missing from standard gan. arXiv preprint. arXiv:1807.00734"},{"key":"1381_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2021.112818","volume":"269","author":"S Li","year":"2022","unstructured":"Li S, Hu G, Cheng X et al (2022) Integrating topographic knowledge into deep learning for the void-filling of digital elevation models. Remote Sens Environ 269:112818","journal-title":"Remote Sens Environ"},{"key":"1381_CR16","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H et\u00a0al (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"key":"1381_CR17","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1007\/s12145-019-00436-6","volume":"13","author":"H Liu","year":"2020","unstructured":"Liu H, Chen S, Hou M et al (2020) Improved inverse distance weighting method application considering spatial autocorrelation in 3d geological modeling. Earth Sci Inf 13:619\u2013632","journal-title":"Earth Sci Inf"},{"issue":"2","key":"1381_CR18","doi-asserted-by":"publisher","first-page":"63","DOI":"10.3390\/geosciences8020063","volume":"8","author":"L Mayer","year":"2018","unstructured":"Mayer L, Jakobsson M, Allen G et al (2018) The nippon foundation-gebco seabed 2030 project: the quest to see the world\u2019s oceans completely mapped by 2030. Geosciences 8(2):63","journal-title":"Geosciences"},{"key":"1381_CR19","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint. arXiv:1411.1784"},{"key":"1381_CR20","doi-asserted-by":"crossref","unstructured":"Nagaraj R, Kumar LS (2024) Extraction of surface water bodies using optical remote sensing images: a review. Earth Sci Inf 1\u201364","DOI":"10.1007\/s12145-023-01196-0"},{"issue":"23","key":"1381_CR21","doi-asserted-by":"publisher","first-page":"2829","DOI":"10.3390\/rs11232829","volume":"11","author":"Z Qiu","year":"2019","unstructured":"Qiu Z, Yue L, Liu X (2019) Void filling of digital elevation models with a terrain texture learning model based on generative adversarial networks. Remote Sensing 11(23):2829","journal-title":"Remote Sensing"},{"issue":"9","key":"1381_CR22","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1080\/13658810601169899","volume":"21","author":"HI Reuter","year":"2007","unstructured":"Reuter HI, Nelson A, Jarvis A (2007) An evaluation of void-filling interpolation methods for srtm data. Int J Geogr Inf Sci 21(9):983\u20131008","journal-title":"Int J Geogr Inf Sci"},{"issue":"2","key":"1381_CR23","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.compag.2005.07.003","volume":"50","author":"T Robinson","year":"2006","unstructured":"Robinson T, Metternicht G (2006) Testing the performance of spatial interpolation techniques for mapping soil properties. Comput Electron Agric 50(2):97\u2013108","journal-title":"Comput Electron Agric"},{"key":"1381_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2","key":"1381_CR25","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1007\/s12145-023-00995-9","volume":"16","author":"I Rousta","year":"2023","unstructured":"Rousta I, Sharif M, Heidari S et al (2023) Climatic variables impact on inland lakes water levels and area fluctuations in an arid\/semi-arid Region of Iran, Iraq, and Turkey based on the remote sensing data. Earth Sci Inf 16(2):1611\u20131635","journal-title":"Earth Sci Inf"},{"key":"1381_CR26","doi-asserted-by":"crossref","unstructured":"Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 23rd ACM national conference, pp 517\u2013524","DOI":"10.1145\/800186.810616"},{"issue":"7","key":"1381_CR27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235487","volume":"15","author":"M Sonogashira","year":"2020","unstructured":"Sonogashira M, Shonai M, Iiyama M (2020) High-resolution bathymetry by deep-learning-based image superresolution. PLoS ONE 15(7):e0235487","journal-title":"PLoS ONE"},{"key":"1381_CR28","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Wu S et al (2018) Esrgan: enhanced super-resolution generative adversarial networks. In: Proceedings of the European conference on computer vision (ECCV) workshops, pp 0\u20130","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"1381_CR29","doi-asserted-by":"publisher","DOI":"10.5285\/f98b053b-0cbc-6c23-e053-6c86abc0af7b","author":"P Weatherall","year":"2023","unstructured":"Weatherall P, Bogonko M, Bringensparr C et al (2023) The gebco 2023 grid - a continuous terrain model of the global oceans and land. NERC EDS British Oceanographic Data Centre NOC. https:\/\/doi.org\/10.5285\/f98b053b-0cbc-6c23-e053-6c86abc0af7b","journal-title":"NERC EDS British Oceanographic Data Centre NOC"},{"issue":"4","key":"1381_CR30","doi-asserted-by":"publisher","first-page":"1756","DOI":"10.1002\/hyp.9719","volume":"28","author":"L Xiong","year":"2014","unstructured":"Xiong L, Tang G, Yan S et al (2014) Landform-oriented flow-routing algorithm for the dual-structure loess terrain based on digital elevation models. Hydrol Process 28(4):1756\u20131766","journal-title":"Hydrol Process"},{"key":"1381_CR31","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.isprsjprs.2019.02.008","volume":"150","author":"Z Xu","year":"2019","unstructured":"Xu Z, Chen Z, Yi W et al (2019) Deep gradient prior network for dem super-resolution: transfer learning from image to dem. ISPRS J Photogramm Remote Sens 150:80\u201390","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1381_CR32","doi-asserted-by":"crossref","unstructured":"Yang J, Zhang Z, Xiao S et al (2023) Efficient data-driven behavior identification based on vision transformers for human activity understanding. Neurocomputing 530:104\u2013115","DOI":"10.1016\/j.neucom.2023.01.067"},{"key":"1381_CR33","doi-asserted-by":"crossref","unstructured":"Yang J, Cheng C, Xiao S et\u00a0al (2023a) High fidelity face-swapping with style convtransformer and latent space selection. IEEE Trans Multimed","DOI":"10.1109\/TMM.2023.3313256"},{"issue":"12","key":"1381_CR34","doi-asserted-by":"publisher","first-page":"734","DOI":"10.3390\/ijgi9120734","volume":"9","author":"C Zhang","year":"2020","unstructured":"Zhang C, Shi S, Ge Y et al (2020) Dem void filling based on context attention generation model. ISPRS Int J Geo Inf 9(12):734","journal-title":"ISPRS Int J Geo Inf"},{"key":"1381_CR35","doi-asserted-by":"crossref","unstructured":"Zhou G, Song B, Liang P et\u00a0al (2022) Voids filling of dem with multiattention generative adversarial network model. Remote Sensing 14(5):1206","DOI":"10.3390\/rs14051206"},{"issue":"4","key":"1381_CR36","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1080\/13658816.2019.1599122","volume":"34","author":"D Zhu","year":"2020","unstructured":"Zhu D, Cheng X, Zhang F et al (2020) Spatial interpolation using conditional generative adversarial neural networks. Int J Geogr Inf Sci 34(4):735\u2013758","journal-title":"Int J Geogr Inf Sci"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01381-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01381-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01381-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T18:19:09Z","timestamp":1729102749000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01381-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,5]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1381"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01381-9","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,5]]},"assertion":[{"value":"19 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No disclosure of potential conflicts of interest and research involving humans or animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"I wish to declare that no conflicts of interest, including personal, academic, political, or other affiliations, might influence the conduct or reporting of the work submitted.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}