Extracting tidally-constrained annual shorelines and robust rates of coastal change from freely available Earth observation data at continental scale
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Updated
Jul 24, 2026 - Jupyter Notebook
Extracting tidally-constrained annual shorelines and robust rates of coastal change from freely available Earth observation data at continental scale
Coastal Observation + Analysis using Satellite-derived Timeseries, Generated Using AI + Real-time Data
An open source package to estimate the coastal variations using R.
Code for computing stratigraphy from bathymetric data, as per Pearson et al (2022): https://doi.org/10.1016/j.geomorph.2022.108185
Jupyter notebook code to generate filmstrips of coastal change using DEA landsat data and geomedians
Spatial prediction of 1990-2024 satellite-derived shoreline-change rates from physical, environmental and defence-related covariates. A focal Bayesian neural network is compared against statistical, geostatistical and machine learning benchmarks.
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