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Deep Learning Image Segmentation of Sandy Beaches in Southeastern Australia

Created 20/10/2024

Updated 20/10/2024

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The collection includes beach coastlines from Southeastern Australia, specifically Victoria and New South Wales, used to train an image segmentation model using the U-Net deep learning architecture for mapping sandy beaches. The dataset contains polygons that represent the outline or extent of the raster images and polygons drawn by citizen-scientists. Additionally, we provide the trained model itself, which can be utilized for further evaluation or refined through fine-tuning. The resulting predictions are also available in Shapefiles format, which can be loaded to NationalMap.

This collection supplements the publication: Regional-Scale Image Segmentation of Sandy Beaches: Comparison of Training and Prediction Across Two Extensive Coastlines in Southeastern Australia (Yong et al.)

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Title Deep Learning Image Segmentation of Sandy Beaches in Southeastern Australia
Language English
Licence notspecified
Landing Page https://devweb.dga.links.com.au/data/dataset/219a0517-bfc7-5ca1-91c8-d41dcdaab992
Contact Point
Environment Protection Authority (EPA) Victoria
CSIROEnquiries@csiro.au
Reference Period 01/01/2000
Geospatial Coverage {"type":"Point","coordinates":[0,0]}
Data Portal data.gov.au