A CGAN-Based Method for Predictive Generation of Wind Environment Images in Stadium-Centered Urban Blocks

A CGAN-Based Method for Predictive Generation of Wind Environment Images in Stadium-Centered Urban Blocks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A CGAN-Based Method for Predictive Generation of Wind Environment Images in Stadium-Centered Urban Blocks Xiaoyang Guo, Shiliang Lu, Wenrui Cao, Lei Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7184998/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Creating a comfortable outdoor environment is crucial for the sustainable development of urban spaces and the quality of human habitation. The agglomeration effect of stadiums often drives surrounding urban blocks toward high-density development. However, the long interface of stadiums combined with dense surrounding buildings frequently leads to local issues such as excessive wind speeds, pollutant accumulation, and uneven ventilation, which negatively impact pedestrian comfort and natural ventilation performance of buildings. Therefore, conducting wind environment assessments in the early planning stage holds significant practical value. Traditional wind environment analysis methods, however, are computationally expensive and inefficient for evaluating and optimizing a large number of design proposals, limiting their applicability in urban design workflows. To address this challenge, this study proposes a CGAN-based deep learning method trained on 80 sets of wind environment cloud maps of high-density stadium-centered urban blocks. The block and stadium heights are encoded using luminance depth. The model’s performance is further evaluated in stadium shape. Experimental results demonstrate that the CGAN model significantly accelerates wind environment prediction while maintaining acceptable accuracy, offering a practical tool for rapid evaluation of early-stage urban planning schemes. This article is supported by the Heilongjiang Provincial Natural Science Foundation Project of the Heilongjiang Provincial Department of Science and Technology, titled "Research on Multi-scenario Conversion Mechanism of Ice and Snow Sports Buildings Driven by Digitalization", with Lei Li as the project leader and the project number being LH2024E049. There are no competing interests in this article.Guo and Cao wrote the main manuscript text and Guo prepared all figures.All authors reviewed the manuscript. Wind environment Conditional Generative Adversarial Network (CGAN)༛Stadium༛Urban design Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7184998","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501817326,"identity":"bb286600-00a1-4c52-9336-a334dd1197e4","order_by":0,"name":"Xiaoyang Guo","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyang","middleName":"","lastName":"Guo","suffix":""},{"id":501817328,"identity":"32661f4d-75aa-44c8-b97e-7a3099a89589","order_by":1,"name":"Shiliang Lu","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shiliang","middleName":"","lastName":"Lu","suffix":""},{"id":501817329,"identity":"bb9fe14d-e321-465d-a8aa-af78e20e6c9b","order_by":2,"name":"Wenrui Cao","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenrui","middleName":"","lastName":"Cao","suffix":""},{"id":501817331,"identity":"fb5af773-ee46-475e-b9bd-f627871bebd7","order_by":3,"name":"Lei Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3RMQrCMBSA4YSAXQJdU8Q7PCi4KHiVF4R2EXF0EIwI7eiae3gBJeBU94IOBS8QcBUxHR2MdXPIvwXykRceIaHQHxYzZhoEwWJBlTuz7yQpiwzsYhwlmm5UJwJVBYm2WQw1od0IqRFSDqafXrZlQ5YjqaLzwSuoRrw5kg6vRzdYlUvF5+glTOChfWU6rOVG0cJIJTh4SU9I1XdkvdcteXYgnBuSaMgYiJaoDkRERQ8sjJlwg2k85WnBZ34yMfG9wYdbpc4ba1ejwS6q/OQ9bH/3w/1QKBQKfegFjK5IjXDoHMYAAAAASUVORK5CYII=","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-22 09:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7184998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7184998/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95525091,"identity":"415ca4a4-ac66-45a3-a783-494435341221","added_by":"auto","created_at":"2025-11-10 10:04:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":482979,"visible":true,"origin":"","legend":"","description":"","filename":"0726.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7184998/v1_covered_1a6e411e-81db-4dc9-9324-2cb712ee1559.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A CGAN-Based Method for Predictive Generation of Wind Environment Images in Stadium-Centered Urban Blocks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wind environment, Conditional Generative Adversarial Network (CGAN)༛Stadium༛Urban design","lastPublishedDoi":"10.21203/rs.3.rs-7184998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7184998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCreating a comfortable outdoor environment is crucial for the sustainable development of urban spaces and the quality of human habitation. The agglomeration effect of stadiums often drives surrounding urban blocks toward high-density development. However, the long interface of stadiums combined with dense surrounding buildings frequently leads to local issues such as excessive wind speeds, pollutant accumulation, and uneven ventilation, which negatively impact pedestrian comfort and natural ventilation performance of buildings. Therefore, conducting wind environment assessments in the early planning stage holds significant practical value. Traditional wind environment analysis methods, however, are computationally expensive and inefficient for evaluating and optimizing a large number of design proposals, limiting their applicability in urban design workflows.\u003c/p\u003e\u003cp\u003eTo address this challenge, this study proposes a CGAN-based deep learning method trained on 80 sets of wind environment cloud maps of high-density stadium-centered urban blocks. The block and stadium heights are encoded using luminance depth. The model\u0026rsquo;s performance is further evaluated in stadium shape. 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