{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:02:23Z","timestamp":1778691743078,"version":"3.51.4"},"reference-count":75,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP19K12681"],"award-info":[{"award-number":["JP19K12681"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The extraction and integration of building facade data are necessary for the development of information infrastructure for urban environments. However, existing methods for parsing building facades based on semantic segmentation have difficulties in distinguishing individual instances of connected buildings. Manually collecting and annotating instances of building facades in large datasets is time-consuming and labor-intensive. With the recent development and use of city digital twins (CDTs), massive high-quality digital assets of buildings have been created. These assets make it possible to generate high-quality and cost-effective synthetic datasets that can replace real-world ones as training sets for the supervised learning-based instance segmentation of building facades. In this study, we developed a novel framework that can automatically produce synthetic datasets from a CDT. An auto-generation system for synthetic street views was built by rendering city digital assets in a game engine, while the system auto-generated the instance annotations for building facades. The hybrid dataset HSRBFIA, along with various subsets containing different proportions of synthetic and real data, were used to train deep learning models for facade instance segmentation. In our experiments, two types of synthetic data (CDT-based and virtual-based) were compared, and the results showed that the CDT synthetic data were more effective in boosting deep learning training with real-world images compared with the virtual synthetic data (no real-world counterparts). By swapping a certain portion of the real data with the proposed CDT synthetic images, the performance could almost match what is achievable when using the real-world training set.<\/jats:p>","DOI":"10.1093\/jcde\/qwac086","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T18:21:33Z","timestamp":1661538093000},"page":"1737-1755","source":"Crossref","is-referenced-by-count":13,"title":["Automatic generation of synthetic datasets from a city digital twin for use in the instance segmentation of building facades"],"prefix":"10.1093","volume":"9","author":[{"given":"Jiaxin","family":"Zhang","sequence":"first","affiliation":[{"name":"Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University , 2-1, Yamadaoka, Suita, Osaka 565-0871, 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