Deep Learning Classification and Feature  Analysis for Ganjiang River Basin Traditional Villages Multicultural Types

Deep Learning Classification and Feature Analysis for Ganjiang River Basin Traditional Villages Multicultural Types | 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 Deep Learning Classification and Feature Analysis for Ganjiang River Basin Traditional Villages Multicultural Types Zhihao LI, Dingfei Li, Wenxuan Lai, Zeyu Liu, Qiliang Cai, Chunqing Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6488547/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Dec, 2025 Read the published version in npj Heritage Science → Version 1 posted 4 You are reading this latest preprint version Abstract This study tackles the challenge of identifying regional cultural features in traditional villages across China's Ganjiang River Basin by proposing a deep learning-based image classification method. Focusing on five cultural types (Hakka, Linchuan, Luling, Yuanzhou, and Yuzhang) as classification labels, we constructed an image dataset and innovatively adapted image recognition architectures for village feature extraction. The research advances three key aspects: (1) A novel framework integrating feature focusing, knowledge transfer, and environmental perturbation optimization, developed through comparative analysis of nine model architectures; (2) Evaluation using Accuracy, Precision, Recall, F1 Score, ROC-AUC, and PRC curves to verify model consistency; (3) Visual interpretation through Grad-CAM, RPN, and UMAP methods. Results demonstrate the modified ConvNeXt model achieves optimal performance, effectively capturing cultural elements in village imagery. This approach enables efficient extraction of regional cultural characteristics from traditional settlements, offering an interpretable technical solution for digitally preserving cultural heritage. Traditional Villages Image Recognition Deep Learning Feature Analysis Regional Culture Ganjiang River Basin Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2025 Read the published version in npj Heritage Science → Version 1 posted Reviewers invited by journal 15 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 20 Apr, 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. We do this by developing innovative software and high quality services for the global research community. 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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-6488547","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471567175,"identity":"baa35863-f506-4716-8dd4-05ed51c75253","order_by":0,"name":"Zhihao LI","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"LI","suffix":""},{"id":471567176,"identity":"3b2d9f72-b5e7-4ea5-9fbb-3c9f687ba63e","order_by":1,"name":"Dingfei Li","email":"","orcid":"","institution":"Jiangxi Agricultural 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