Coastal zone classification and shoreline property identification based on random forests with dual-band LiDAR data

Coastal zone classification and shoreline property identification based on random forests with dual-band LiDAR data | 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 Coastal zone classification and shoreline property identification based on random forests with dual-band LiDAR data Huaigang Jiang, Zhenchao Zhang, Ying Yu, Chenguang Dai, Ning Yi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5275554/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 Effective utilization of the coastal zone cannot be achieved without the support of strong mapping technology. With the growing technological advancements, LiDAR technology has found widespread applications in coastal zone mapping. Dual-band LiDAR integrates green and NIR wavelength lasers, offering the capability of acquiring both coastal terrain geometry and dual-band intensity simultaneously, providing complementary data that are temporally and spatially consistent.This study presents a framework for fusing dual-band echo intensity features together with geometric features for coastal zone classification and property identification of the extracted shoreline key points. It can directly extract the shoreline key points with property labels, which is a prerequisite for generating shorelines. Thus, the method provides an effective practical reference for more accurate measurement of the shoreline. Specifically, 26-dimensional geometric features are extracted from the coastal zone point cloud; the point cloud is then classified into five categories using the Random Forests; afterwards, the shoreline key point are extracted based on multiple coarse-to-fine grid constraints; finally, the shoreline key point are classified into three types. Experiments on the dual-band LiDAR data verifies that additional usage of dual-band echo intensity features improves the accuracy of coastal point cloud classification and shoreline property identification. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Ocean sciences dual-band LiDAR coastal classification shoreline key point property identification Random Forests 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-5275554","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379640405,"identity":"cde68924-fe8d-4875-a26e-c90aa19a9164","order_by":0,"name":"Huaigang Jiang","email":"","orcid":"","institution":"Information Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Huaigang","middleName":"","lastName":"Jiang","suffix":""},{"id":379640406,"identity":"a7abe2bc-7f57-47c3-a6c9-893d73564151","order_by":1,"name":"Zhenchao 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