{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T06:14:01Z","timestamp":1770876841629,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education","award":["RS-2023-00249407"],"award-info":[{"award-number":["RS-2023-00249407"]}]},{"name":"Korea government","award":["RS-2024-00336025"],"award-info":[{"award-number":["RS-2024-00336025"]}]},{"name":"Korea government","award":["RS-2025-00558871"],"award-info":[{"award-number":["RS-2025-00558871"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Urban green-space monitoring in dense cityscapes remains limited by accuracy\u2013efficiency trade-offs and the absence of integrated, auditable area estimation. We introduce GreenViT, a Vision Transformer (ViT) based framework for precise segmentation and transparent quantification of urban green space. GreenViT couples a ViT-L\/14 backbone with a lightweight single-path, progressive upsampling decoder (Green Head), preserving global context while recovering thin structures. Experiments were conducted on a manually annotated dataset of 20 high-resolution satellite images collected from Satellites.Pro, covering five land-cover classes (background, green space, building, road, and water). Using a 224 \u00d7 224 sliding window sampling scheme, the 20 images yield 62,650 training\/validation patches. Under five-fold evaluation, it attains 0.9200 \u00b1 0.0243 mIoU, 0.9580 \u00b1 0.0135 Dice, and 0.9570 PA, and the calibrated estimator achieves 1.10% relative area error. Overall, GreenViT strikes a strong balance between accuracy and efficiency, making it particularly well-suited for thin or boundary-rich classes. It can be used to support planning evaluations, green-space statistics, urban renewal assessments, and ecological red-line verification, while providing reliable green-area metrics to support urban heat mitigation and pollution control efforts. This makes it highly suitable for decision-oriented long-term monitoring and management assessments.<\/jats:p>","DOI":"10.3390\/jimaging12020072","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T09:16:08Z","timestamp":1770801368000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GreenViT: A Vision Transformer with Single-Path Progressive Upsampling for Urban Green-Space Segmentation and Auditable Area Estimation"],"prefix":"10.3390","volume":"12","author":[{"given":"Ziqiang","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Robot and Smart System Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young","family":"Choi","sequence":"additional","affiliation":[{"name":"Earth Turbine, 36, Dongdeok-ro 40-gil, Jung-gu, Daegu 41905, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6733-8597","authenticated-orcid":false,"given":"Changyong","family":"Yi","sequence":"additional","affiliation":[{"name":"Intelligent Construction Automation Center, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chanjeong","family":"Park","sequence":"additional","affiliation":[{"name":"Intelligent Construction Automation Center, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8069-0659","authenticated-orcid":false,"given":"Jinyoung","family":"Park","sequence":"additional","affiliation":[{"name":"Intelligent Construction Automation Center, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyungkeun","family":"Park","sequence":"additional","affiliation":[{"name":"Intelligent Construction Automation Center, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sujeen","family":"Song","sequence":"additional","affiliation":[{"name":"Earth Turbine, 36, Dongdeok-ro 40-gil, Jung-gu, Daegu 41905, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102869","DOI":"10.1016\/j.habitatint.2023.102869","article-title":"Evaluating trends, profits, and risks of global cities in recent urban expansion for advancing sustainable development","volume":"131","author":"Zhong","year":"2023","journal-title":"Habitat Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101563","DOI":"10.1016\/j.uclim.2023.101451","article-title":"Multi-scale climate-sensitive planning framework to mitigate urban heat island effect: A case study in Singapore","volume":"49","author":"Zhang","year":"2023","journal-title":"Urban Clim."},{"key":"ref_3","first-page":"172","article-title":"Impact of urban heat island on human health: A systematic review","volume":"24","author":"Ramly","year":"2024","journal-title":"Malays. J. Public Health Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108005","DOI":"10.1016\/j.envint.2023.108005","article-title":"Urban heat island impacts on heat-related cardiovascular morbidity: A time series analysis of older adults in US metropolitan areas","volume":"178","author":"Cleland","year":"2023","journal-title":"Environ. Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100433","DOI":"10.1016\/j.ancene.2024.100433","article-title":"Assessing the effects of urban heat islands and air pollution on human quality of life","volume":"46","author":"Cichowicz","year":"2024","journal-title":"Anthropocene"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"174650","DOI":"10.1016\/j.scitotenv.2024.174650","article-title":"Heat exposure impacts on urban health: A meta-analysis","volume":"947","author":"Yang","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_7","unstructured":"World Health Organization (2026, January 25). WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. Available online: https:\/\/iris.who.int\/handle\/10665\/345329."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"127932","DOI":"10.1016\/j.ufug.2023.127932","article-title":"Urban green spaces and sustainability: Exploring the ecosystem services and disservices of grassy lawns versus floral meadows","volume":"84","author":"Paudel","year":"2023","journal-title":"Urban For. Urban Green."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103556","DOI":"10.1016\/j.habitatint.2025.103556","article-title":"Marginalized but equal? An investigation of visible green equity disparities in marginalized residents\u2019 daily commutes and its potential green solutions","volume":"165","author":"Jin","year":"2025","journal-title":"Habitat Int."},{"key":"ref_10","unstructured":"Lee, H., Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P.W., Trisos, C., Romero, J., Aldunce, P., and Barrett, K. (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC. Available online: https:\/\/www.ipcc.ch\/report\/ar6\/syr\/downloads\/report\/IPCC_AR6_SYR_FullVolume.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, W., and Chen, M. (2024). Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning. Buildings, 14.","DOI":"10.3390\/buildings14030835"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106139","DOI":"10.1016\/j.cities.2025.106139","article-title":"Machine learning applications for urban geospatial analysis: A review of urban and environmental studies","volume":"165","author":"Shaamala","year":"2025","journal-title":"Cities"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106102","DOI":"10.1016\/j.cities.2025.106102","article-title":"Urban land use mix and AI: A systematic review","volume":"165","author":"Drici","year":"2025","journal-title":"Cities"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1080\/01431161.2021.1881185","article-title":"Advancements in the remote sensing of landscape pattern of urban green spaces and vegetation fragmentation","volume":"42","author":"Kowe","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1080\/17538947.2023.2207839","article-title":"A labor-free index-guided semantic segmentation approach for urban vegetation mapping from high-resolution true color imagery","volume":"16","author":"Zhang","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fu, W., Chen, Z., Cheng, Q., Li, Y., Zhai, W., Ding, F., Kuang, X., Chen, D., and Duan, F. (2025). Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision. Agriculture, 15.","DOI":"10.3390\/agriculture15121272"},{"key":"ref_17","first-page":"4408820","article-title":"Transformer and CNN hybrid deep neural network for semantic segmentation of very-high-resolution remote sensing imagery","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6004105","DOI":"10.1109\/LGRS.2023.3261402","article-title":"A self-learning-update CNN model for semantic segmentation of remote sensing images","volume":"20","author":"Zheng","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5896","DOI":"10.1080\/01431161.2023.2255354","article-title":"CNNs in land cover mapping with remote sensing imagery: A review and meta-analysis","volume":"44","author":"Kotaridis","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12597","DOI":"10.1038\/s41598-024-63363-7","article-title":"Local feature acquisition and global context understanding network for very high-resolution land cover classification","volume":"14","author":"Li","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7682","DOI":"10.1109\/TPAMI.2024.3392941","article-title":"A survey on efficient vision transformers: Algorithms, techniques, and performance benchmarking","volume":"46","author":"Papa","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","first-page":"104546","article-title":"CTSeg: CNN and ViT collaborated segmentation framework for efficient land-use\/land-cover mapping with high-resolution remote sensing images","volume":"139","author":"Chen","year":"2025","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s10916-024-02105-8","article-title":"Comparison of vision transformers and convolutional neural networks in medical image analysis: A systematic review","volume":"48","author":"Takahashi","year":"2024","journal-title":"J. Med. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104653","DOI":"10.1016\/j.scs.2023.104653","article-title":"Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework","volume":"96","author":"Li","year":"2023","journal-title":"Sustain. Cities Soc."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Neyns, R., and Canters, F. (2022). Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review. Remote Sens., 14.","DOI":"10.3390\/rs14041031"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, N., Quan, H., He, J., Li, S., Xiao, M., Wang, B., Chen, T., Dai, X., Pan, J., and Li, N. (2023). Urban Vegetation Extraction from High-Resolution Remote Sensing Imagery on SD-UNet and Vegetation Spectral Features. Remote Sens., 15.","DOI":"10.3390\/rs15184488"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107123","DOI":"10.1016\/j.compag.2022.107123","article-title":"Improving vegetation segmentation with shadow effects based on double input networks using polarization images","volume":"199","author":"Yang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","first-page":"172","article-title":"Semantic segmentation of agricultural images: A survey","volume":"11","author":"Luo","year":"2024","journal-title":"Inf. Process. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3583","DOI":"10.1080\/01431161.2021.1876272","article-title":"Dual attention deep fusion semantic segmentation networks of large-scale satellite remote-sensing images","volume":"42","author":"Li","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","first-page":"103514","article-title":"Multi-scale feature fusion and transformer network for urban green space segmentation from high-resolution remote sensing images","volume":"124","author":"Cheng","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1109\/JSTARS.2025.3527226","article-title":"ViT-ISRGAN: A high-quality super-resolution reconstruction method for multispectral remote sensing images","volume":"18","author":"Yang","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ali, A.M., Benjdira, B., Koubaa, A., El-Shafai, W., Khan, Z., and Boulila, W. (2023). Vision Transformers in Image Restoration: A Survey. Sensors, 23.","DOI":"10.3390\/s23052385"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"117673","DOI":"10.1016\/j.powtec.2022.117673","article-title":"TESN: Transformers enhanced segmentation network for accurate nanoparticle size measurement of TEM images","volume":"407","author":"Wang","year":"2022","journal-title":"Powder Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Aleissaee, A.A., Kumar, A., Anwer, R.M., Khan, S., Cholakkal, H., Xia, G.-S., and Khan, F.S. (2023). Transformers in Remote Sensing: A Survey. Remote Sens., 15.","DOI":"10.3390\/rs15071860"},{"key":"ref_35","first-page":"64","article-title":"Transformers in small object detection: A benchmark and survey of state-of-the-art","volume":"58","author":"Rekavandi","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Su, Y., Cheng, J., Bai, H., Liu, H., and He, C. (2022). Semantic Segmentation of Very-High-Resolution Remote Sensing Images via Deep Multi-Feature Learning. Remote Sens., 14.","DOI":"10.3390\/rs14030533"},{"key":"ref_37","unstructured":"(2025, February 21). Satellites.pro. Available online: https:\/\/satellites.pro\/."},{"key":"ref_38","unstructured":"CCF (2026, January 25). Satellite Imagery AI Classification and Recognition Dataset. DataFountain. Available online: https:\/\/www.datafountain.cn\/competitions\/270\/datasets\/."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/72\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T05:18:56Z","timestamp":1770873536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,10]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["jimaging12020072"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12020072","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,10]]}}}