{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:46:10Z","timestamp":1760121970710,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,5]],"date-time":"2023-02-05T00:00:00Z","timestamp":1675555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906039","2242022k30007"],"award-info":[{"award-number":["61906039","2242022k30007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Youth Scholar Program of SEU","award":["61906039","2242022k30007"],"award-info":[{"award-number":["61906039","2242022k30007"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["61906039","2242022k30007"],"award-info":[{"award-number":["61906039","2242022k30007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Large-scale geospatial data pave the way for geospatial machine learning algorithms, and a good representation is related to whether the machine learning model is effective. Hence, it is a critical task to learn effective feature representation for geospatial data. In this paper, we construct a spatial graph from the locations and propose a geospatial graph contrastive learning method to learn the location representations. Firstly, we propose a skeleton graph in order to preserve the primary structure of the geospatial graph to solve the positioning bias problem of remote sensing. Then, we define a novel mixed node centrality measure and propose four data augmentation methods based on the measure. Finally, we propose a heterogeneous graph attention network to aggregate information from both the structural neighborhood and semantic neighborhood separately. Extensive experiments on both geospatial datasets and non-geospatial datasets are conducted to illustrate that the proposed method outperforms state-of-the-art baselines.<\/jats:p>","DOI":"10.3390\/rs15040880","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T05:29:05Z","timestamp":1675661345000},"page":"880","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Representing Spatial Data with Graph Contrastive Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Lanting","family":"Fang","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing 210096, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ze","family":"Kou","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing 210096, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.neucom.2020.07.144","article-title":"ResGNet-C: A graph convolutional neural network for detection of COVID-19","volume":"452","author":"Yu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102439","DOI":"10.1016\/j.ipm.2020.102439","article-title":"Improved breast cancer classification through combining graph convolutional network and convolutional neural network","volume":"58","author":"Zhang","year":"2021","journal-title":"Inf. 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