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The following results are noted: (i) As the training set size grows, models\u2019 accuracies are improved, particularly for multi-layer perceptron (MLP) or random forest (RF). The graph convolutional network (GCN) (MLP or RF) model reveals top accuracy when the proportion of training samples is less (greater) than 10% of the total number of functional areas; (ii) With a large amount of training samples, MLP shows the highest prediction accuracy and good performances in cross-validation, but less stability on same training sets; (iii) With a small amount of training samples, GCN provides viable results, by incorporating the auxiliary information provided by the proposed semantic linkages, which is meaningful in real-world predictions; (iv) When the training samples are less than 10%, one should be cautious using MLP to test the optimal epoch for obtaining the best accuracy, due to its model overfitting problem. The above insights could support efficient and scalable urban functional area mapping, even with insufficient land-use information (e.g., covering only ~20% of Beijing in the case study).<\/jats:p>","DOI":"10.3390\/rs15020341","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T03:31:28Z","timestamp":1672975888000},"page":"341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Supervised versus Semi-Supervised Urban Functional Area Prediction: Uncertainty, Robustness and Sensitivity"],"prefix":"10.3390","volume":"15","author":[{"given":"Rui","family":"Deng","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yanning","family":"Guan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8986-1354","authenticated-orcid":false,"given":"Danlu","family":"Cai","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Tao","family":"Yang","sequence":"additional","affiliation":[{"name":"The School of Architecture, Tsinghua University, Beijing 100084, China"}]},{"given":"Klaus","family":"Fraedrich","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Meteorology, 20146 Hamburg, Germany"}]},{"given":"Chunyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7069-1044","authenticated-orcid":false,"given":"Jiakui","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Yanshan Earth Key Zone and Surface Flux Observation and Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Zhouwei","family":"Liao","sequence":"additional","affiliation":[{"name":"Yangtze River Basin Operation Management Center, China Three Gorges, Co., Ltd., Yichang 443133, China"}]},{"given":"Zhishou","family":"Wei","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Shan","family":"Guo","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Duranton, G., Henderson, J.V., and Strange, W.C. 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