{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T04:58:22Z","timestamp":1773723502533,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Climate Service (ACS)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Beaches play a crucial role in recreation and ecosystem habitats, and are central to Australia\u2019s national identity. Precise mapping of beach locations is essential for coastal vulnerability and risk assessments. While point locations of over 11,000 beaches are documented from citizen science mapping projects, the full spatial extent and outlines of many Australian beaches remain unmapped. This study leverages deep learning (DL), specifically convolutional neural networks, for binary image segmentation to map beach outlines along the coast of Southeastern Australia. It focuses on Victoria and New South Wales coasts, each approximately 2000 to 2500 km in length. Our methodology includes training and evaluating the model using state-specific datasets, followed by applying the trained model to predict the beach outlines, size, shape, and morphology in both regions. The results demonstrate the model\u2019s ability to generate accurate segmentation and rapid predictions, although it faces challenges such as misclassifying cliffs and sensitivity to fine details. Overall, this research presents a significant advancement in integrating DL with coastal science, providing a scalable solution of citizen science mapping efforts for comprehensive beach mapping to support sustainable coastal management and conservation efforts across Australia. Open access datasets and models are provided to further support beach mapping efforts around Australia.<\/jats:p>","DOI":"10.3390\/rs16183534","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T03:49:46Z","timestamp":1727149786000},"page":"3534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Regional-Scale Image Segmentation of Sandy Beaches in Southeastern Australia"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5204-2902","authenticated-orcid":false,"given":"Suk Yee","family":"Yong","sequence":"first","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation Information Management & Technology Scientific Computing, Eveleigh, NSW 2015, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3552-9193","authenticated-orcid":false,"given":"Julian","family":"O\u2019Grady","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation Environment, Aspendale, VIC 3195, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0137-1578","authenticated-orcid":false,"given":"Rebecca","family":"Gregory","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation Environment, Aspendale, VIC 3195, Australia"}]},{"given":"Dylan","family":"Lynton","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation Environment, Aspendale, VIC 3195, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/S0964-5691(00)00040-5","article-title":"From beaches to beach environments: Linking the ecology, human-use and management of beaches in Australia","volume":"43","author":"James","year":"2000","journal-title":"Ocean Coast. 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