{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T15:27:43Z","timestamp":1767108463349,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Evaluating solar radiation distribution at the urban scale is crucial for optimizing the placement and size of solar installations and managing urban heat. This study introduces a method for predicting urban solar radiation using 2D mapping data, applying a Generative Adversarial Network (GAN) model to the city of Boston. Traditional solar radiation simulation methods, such as 3D modeling and satellite imagery, require complex and resource-intensive data inputs. In contrast, this research allows open-source 2D urban geographic information\u2014such as building footprints, heights, and terrain\u2014to predict solar radiation at various spatial scales (150 m, 300 m, and 500 m). The GAN model, using detailed 3D urban modeling and simulation results, trained paired datasets of geographic information and solar radiation heatmaps. It achieved high accuracy and resolution, with the 300 m scale model demonstrating the best performance (R2 = 0.864). The model\u2019s capability to generate high-resolution (2 m) solar radiation maps from simplified inputs demonstrates the potential of GANs for urban climate data prediction, offering a rapid and efficient alternative to traditional methods. This approach holds significant potential for urban planning, particularly in optimizing photovoltaic (PV) system layouts and managing the UHI effect.<\/jats:p>","DOI":"10.3390\/rs16234524","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T04:04:04Z","timestamp":1733198644000},"page":"4524","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SolarGAN for Meso-Level Solar Radiation Prediction at the Urban Scale: A Case Study in Boston"],"prefix":"10.3390","volume":"16","author":[{"given":"Yijun","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Architecture, National University of Singapore, Singapore 117566, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3647-8833","authenticated-orcid":false,"given":"Xinru","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Urban Planning and Design, The University of Hong Kong, Hong Kong SAR, China"}]},{"given":"Siyuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Bartlett School of Planning, University College London, London WC1E 6BT, UK"}]},{"given":"Yuankai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Urban Planning and Design, The University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6461-7243","authenticated-orcid":false,"given":"Waishan","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Urban Planning and Design, The University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5062-0270","authenticated-orcid":false,"given":"Da","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bath, Bath BA2 7AY, UK"}]},{"given":"Yifan","family":"Li","sequence":"additional","affiliation":[{"name":"Global Innovation Exchange Institute, Tsinghua University, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"146389","DOI":"10.1016\/j.scitotenv.2021.146389","article-title":"Urban Heat Island (UHI) Intensity and Magnitude Estimations: A Systematic Literature Review","volume":"779","author":"Kim","year":"2021","journal-title":"Sci. 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