Harnessing deep learning and satellite-derived data for short-term, real-time coastal impact predictions (data)

Muir, F. M. E. (2025) Harnessing deep learning and satellite-derived data for short-term, real-time coastal impact predictions (data). [Data Collection]

Collection description

This dataset provides the research basis of the thesis chapter (and eventual journal article) of the same name. It is made up of one folder, one Python script, one pickle file holding a Python object, and one README text file. All data, both original and derived (i.e. stored in database formats), are from publicly available sources. All analyses and data queries should be run using the COASTGUARD Python toolbox: https://github.com/fmemuir/COASTGUARD. The contents are listed below.
• CoastLearn_StAndrewsFull_README.txt: in-depth description of data;
• CoastLearn_Driver_StAndrewsFull.py: COASTGUARD CoastLearn driver file file for obtaining the attached data and running the analyses associated with the publication;
• StAndrewsEastS2Full2024_FullPredict.pkl: serialised Python object file, saved using the Python package pickle (v4.0). The file holds resulting outputs from the data-driven shoreline prediction analysis;
• transect_intersections:
o 4 x serialised Python object files, generated using the COASTGUARD CoasTrack functions and saved using the Python package pickle (v4.0). The base file “StAndrewsEastS2Full2024_transect_intersects.pkl” holds a Python variable of GeoDataFrame type, representing cross-shore transects across the outer Eden Estuary and vegetation edge statistics from intersecting each transect with vegetation edges derived from Sentinel-2 satellite imagery using the COASTGUARD tool VedgeSat;
“_water_intersects.pkl” is the same transect GeoDataFrame but holding statistics from intersecting each transect with waterlines derived from Sentinel-2 satellite imagery using the CoastSat toolbox (housed within COASTGUARD);
o “_wave_intersects.pkl” is the transect GeoDataFrame intersected with statistics from the gridded timeseries of offshore wave conditions from Copernicus Marine Service;
o “_topo_intersects.pkl” is the transect GeoDataFrame holding statistics from intersection with Scottish Government Phase 5 lidar.

Funding:
College / School: College of Science and Engineering > School of Geographical and Earth Sciences > Earth Sciences
Date Deposited: 13 May 2025 07:48
URI: https://researchdata.gla.ac.uk/id/eprint/1963

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Data

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File size:367MB
License:CC BY 4.0

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Muir, F. M. E. (2025); Harnessing deep learning and satellite-derived data for short-term, real-time coastal impact predictions (data)

University of Glasgow

DOI: 10.5525/gla.researchdata.1963

Retrieved: 2025-06-16

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