Data

Global coastal geomorphology dataset based on machine learning methods

The University of Queensland
Dr Daniel Harris (Author) Mr Yongjing Mao (Author) Professor Stuart Phinn (Author)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/f60606a&rft.title=Global coastal geomorphology dataset based on machine learning methods&rft.identifier=10.48610/f60606a&rft.publisher=The University of Queensland&rft.description=This dataset is related to the paper “Global Coastal Geomorphology – Integrating Earth Observation and Geospatial Data” published in Remote Sensing of Environment. It classifies coastal geomorphology around the world into beach, bedrock and wetland with machine learning methods based on satellite images and existing geospatial datasets.&rft.creator=Dr Daniel Harris&rft.creator=Mr Yongjing Mao&rft.creator=Professor Stuart Phinn&rft.date=2022&rft.type=dataset&rft.language=English Access the data

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Open Access

Permitted Re-use with Acknowledgement

Contact Information

s4522477@student.uq.edu.au

Full description

This dataset is related to the paper “Global Coastal Geomorphology – Integrating Earth Observation and Geospatial Data” published in Remote Sensing of Environment. It classifies coastal geomorphology around the world into beach, bedrock and wetland with machine learning methods based on satellite images and existing geospatial datasets.

Issued: 2022

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