Semantic segmentation of historic landscape system along Jiangnan Canal based on deep learning and multi-modal geodata

Semantic segmentation of historic landscape system along Jiangnan Canal based on deep learning and multi-modal geodata | 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 Semantic segmentation of historic landscape system along Jiangnan Canal based on deep learning and multi-modal geodata Li Ran, Keyu Chen, Shunhan Zhang, Qianting Gao, Yuqi Gao, Shangyu Tan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7341579/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Dec, 2025 Read the published version in npj Heritage Science → Version 1 posted 12 You are reading this latest preprint version Abstract The Jiangnan Canal historical landscape system has sustained socioeconomic development for millennia. To better extract heritage landscape value from historical imagery, a Geo-SegFormer historical landscape semantic segmentation method integrating deep learning with multi-modal geospatial data is proposed. Firstly, the panchromatic satellite imagery is enhanced into three differentiated bands using the CLAHE algorithm. Subsequently, based on the formation mechanism of canal-associated landscapes, topographic and hydrographic data are introduced as auxiliary features and fused with enhanced images to generate five-channel inputs. Additionally, dynamic channel scaling, dynamic weight loading, and parameter migration mechanisms are designed to enable the SegFormer model to automatically adapt to tasks with arbitrary input channels and class counts while retaining pretrained weights. Finally, using the self-constructed dataset, the proposed method restores 35 fine-grained landscape categories at 1-meter resolution across the entire Jiangnan Canal region. Ablation experiments and XGBoost-SHAP interpretation further confirm the model’s effectiveness. This research advances historical panchromatic imagery application for interpreting the Jiangnan Canal landscape system while providing a flexible framework for incorporating additional multi-modal data in future semantic segmentation tasks. This research not only advances the application of historical panchromatic imagery to interpret the Jiangnan Canal landscape system but also provides a flexible framework adaptable to additional multi-modal data for diverse future segmentation tasks. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Dec, 2025 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 15 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviews received at journal 26 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers invited by journal 13 Sep, 2025 Editor assigned by journal 18 Aug, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 10 Aug, 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-7341579","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":517298873,"identity":"44251632-0699-4801-9e2b-ec7ea7085471","order_by":0,"name":"Li Ran","email":"","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Ran","suffix":""},{"id":517298874,"identity":"b8d6e730-57d4-4f2c-9fb7-5c13635b0edc","order_by":1,"name":"Keyu Chen","email":"","orcid":"","institution":"Sichuan Institute of Computer 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