{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T10:56:30Z","timestamp":1778324190338,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2024YFC3807900"],"award-info":[{"award-number":["2024YFC3807900"]}]},{"name":"National Key R&amp;D Program of China","award":["52394224"],"award-info":[{"award-number":["52394224"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["2024YFC3807900"],"award-info":[{"award-number":["2024YFC3807900"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["52394224"],"award-info":[{"award-number":["52394224"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on urban morphology discovery have always been based on raster analysis and have been limited to the central city area. Raster analysis can lead to fragmented forms, and focusing only on the central city area ignores many representative urban forms in the suburbs and towns. In this study, a vast and complex dataset was applied to the urban morphology discovery based on the administrative community or village boundary, and a new image deformation pipeline was proposed to enhance the morphological characteristics of building groups. This allows visual neural networks to focus on extracting the morphological characteristics of building groups. Additionally, the research on urban morphology often uses unsupervised learning, which means that the learning process is difficult to control. Therefore, we refined the NT-Xent loss so that it can integrate morphological indicators. This improvement allows the visual neural network to \u201crecognize\u201d the similarity of samples during optimization. By defining the similarity, we can guide the network to bring samples closer or move them farther apart based on certain morphological indicators. Three Chinese cities were used for our testing. Representative urban types were identified, particularly some types located at the urban fringe. The data analysis demonstrated the effectiveness of our image deformation pipeline and loss function, and the sociological analysis illustrated the unique urban functions of these urban types.<\/jats:p>","DOI":"10.3390\/ijgi14050196","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T04:06:21Z","timestamp":1746677181000},"page":"196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Contrastive Learning with Image Deformation and Refined NT-Xent Loss for Urban Morphology Discovery"],"prefix":"10.3390","volume":"14","author":[{"given":"Chunliang","family":"Hua","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daijun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Architecture, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9269-9560","authenticated-orcid":false,"given":"Mengyuan","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Architecture, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-0472","authenticated-orcid":false,"given":"Qiao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"School of Economics and Management, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hillier, B., and Hanson, J. 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