Data and code underlying the PhD thesis: Deep Learning and Earth Observation for the Study of West African Rainfall

doi:10.4121/0581dd0b-bfe8-466c-b7f7-dffe55ed28b5.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/0581dd0b-bfe8-466c-b7f7-dffe55ed28b5
Datacite citation style:
Estebanez Camarena, Monica (2024): Data and code underlying the PhD thesis: Deep Learning and Earth Observation for the Study of West African Rainfall. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/0581dd0b-bfe8-466c-b7f7-dffe55ed28b5.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

The PhD thesis "Deep Learning and Earth Observation for the Study of West African Rainfall" develops a Deep Learning-based satellite rainfall retrieval model for West Africa, called "RainRunner". RainRunner classifies 3-hour sequences of Meteosat Second Generation (MSG) WV and TIR images in rain/no-rain. After being trained in Northern Ghana, RainRunner is applied to a wider area in West Africa (the Sudanian Savana), to evaluate generalization capability and understand better the rainfall mechanisms in the wider area. This dataset allows to do a full performance evaluation of the model by downloading and processing MSG data to create the test dataset, applying the model and evaluating the results. More information about the exact goal of each script can be found the README.txt file.

history
  • 2024-12-10 first online, published, posted
publisher
4TU.ResearchData
format
scripts/ipynb; compressed dataset (tar): objects/npy and spreadsheets/csv
organizations
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water Management

DATA

files (17)