Automatic Classification and Style Analysis ofLandscape Architecture Images Based on DeepLearning

Automatic Classification and Style Analysis ofLandscape Architecture Images Based on DeepLearning | 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 Article Automatic Classification and Style Analysis ofLandscape Architecture Images Based on DeepLearning Han Yang, Dong Zheng, Lianyu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7144298/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract In the realm of computational sciences, the integration of advanced image analysis techniques has become pivotal foraddressing complex classification and style analysis tasks. Traditional methodologies often rely on manual feature extractionand heuristic rules, which can be labor-intensive and prone to subjective biases, thereby limiting scalability and consistency. Toovercome these limitations, we introduce an innovative framework that leverages deep learning architectures to automatethe classification and style analysis of images. Our approach employs a multi-layered convolutional neural network (CNN)designed to capture intricate patterns and stylistic elements inherent in visual data. By training this model on a curated datasetencompassing diverse image categories and stylistic variations, we enable the automatic extraction of high-level features,facilitating precise categorization and nuanced style differentiation. The proposed system integrates attention mechanisms toenhance the model’s sensitivity to fine-grained stylistic cues, thereby improving its interpretability and generalization acrossvaried visual contexts. Experimental evaluations conducted on multiple benchmark datasets demonstrate that our methodsignificantly outperforms conventional techniques in both accuracy and computational efficiency. These findings underscore thepotential of deep learning to revolutionize image analysis within computational sciences, offering a robust tool for researchersand practitioners aiming to enhance interpretability, scalability, and decision-making processes in complex visual domains.The framework not only reduces reliance on expert-driven feature engineering but also opens new avenues for scalable,data-driven visual understanding in interdisciplinary applications such as digital humanities, medical imaging, and culturalheritage preservation. Physical sciences/Engineering Physical sciences/Mathematics and computing Deep Learning Image Classification Style Analysis Convolutional Neural Networks Computational Sciences Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 29 Sep, 2025 Reviews received at journal 27 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviews received at journal 22 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor invited by journal 18 Jul, 2025 Editor assigned by journal 18 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 16 Jul, 2025 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. 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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-7144298","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":518907744,"identity":"ded60d38-e1fa-43b4-9575-73628d33e045","order_by":0,"name":"Han Yang","email":"","orcid":"","institution":"Shunde Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Yang","suffix":""},{"id":518907745,"identity":"5eb4604a-7135-41ba-9d62-111da7756079","order_by":1,"name":"Dong Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYNACg392/MyMjQ+ATBkGBjZitBQcSJZsbz5sANTMQ6SWDwcYN5w5liZBlBb+9t5jEm8M7jBLzsgxq/jY9oeHn70tgeFHxTacWiTOnEs2nGPwjI9fIsfs5sw2Ax7JnmMHGHvO3MapxUAix/AxjwEz2JbbvEAtBjfSG5gZ2/BqMTgM1MK44UaOWTGxWkC2HAZ7nxmiJe0AXi0SZ84YA/2SBg5kyRnnjEF+STiIzy/87T1mEm/+2ICj8sOHMjk5YIgZPvhRgVsLGPCgCxzArx6bllEwCkbBKBgFyAAA6G1VFlOnH6kAAAAASUVORK5CYII=","orcid":"","institution":"Guangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Dong","middleName":"","lastName":"Zheng","suffix":""},{"id":518907747,"identity":"3d51d9f8-4d39-440d-8069-6d45425a3d8f","order_by":2,"name":"Lianyu Wang","email":"","orcid":"","institution":"Sichuan University of Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Lianyu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-17 02:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7144298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7144298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92062808,"identity":"df76fa2d-aa31-47e4-a641-fbf04bc83af0","added_by":"auto","created_at":"2025-09-24 08:31:10","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5534,"visible":true,"origin":"","legend":"","description":"","filename":"67a3da56b90b41ef98a816dad3362253.json","url":"https://assets-eu.researchsquare.com/files/rs-7144298/v1/f2b645a24097923adf1fc3ff.json"},{"id":92064154,"identity":"33e1073b-2aa2-44a1-b8f6-4ab2cb7ea243","added_by":"auto","created_at":"2025-09-24 08:39:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2306811,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7144298/v1_covered_f41da767-c447-4b7b-a50f-ad0cb859f6f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automatic Classification and Style Analysis ofLandscape Architecture Images Based on DeepLearning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep Learning, Image Classification, Style Analysis, Convolutional Neural Networks, Computational Sciences","lastPublishedDoi":"10.21203/rs.3.rs-7144298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7144298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the realm of computational sciences, the integration of advanced image analysis techniques has become pivotal foraddressing complex classification and style analysis tasks. 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