Floods in coastal areas can be extremely destructive natural hazards resulting in societal and economical damage. In this tutorial, explore how to predict building and population density to understand the potential impact from a flooding event.
Authors:
- Casper Fibaek, European Space Agency Φ-lab, Casper.Fibaek@esa.int
- Andreas Luyts, European Space Agency Φ-lab, Andreas.Luyts@ext.esa.int
- Nirdesh Kumar Sharma, EarthSense Labs, nirdesh@earthsenselabs.com
Originally presented at Climate Change AI Summer School 2023, revised for the Summer School 2026
We recommend executing these notebooks in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
Part I:
Part II:
Estimated time to execute end-to-end: 30 minutes
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Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.
Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
Fibaek, C., Luyts, A., Sharma, N. (2026). Sea Water Flood Risk Assessment in Egypt using Deep Learning, Sentinel-1 & 2, and Copernicus DEM [Tutorial]. In Climate Change AI Summer School 2026. Climate Change AI. https://doi.org/10.5281/zenodo.21982751
@misc{fibaek2026sea,
title={Sea Water Flood Risk Assessment in Egypt using Deep Learning, Sentinel-1 & 2, and Copernicus DEM},
author={Fibaek, Casper and Luyts, Andreas and Sharma, Nirdesh},
year={2026},
howpublished={\url{https://github.com/climatechange-ai-tutorials/flood-monitoring}},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.21982751},
booktitle={Climate Change AI Summer School 2026}
}