Poster De Conférence Année : 2023

Geoscientific benchmarks for AI methods.

Alexis Plunder
Alex Vella

Résumé

The BRGM has a wealth of geoscientific data. Through the AMIGO research project, we wish to make part of these data available to the scientific community in order to advance the development of AI methods adapted to geoscientific problems. In this document, we focus on the following problems: ground water level forecasting, mineral potential mapping, automatic mineral detection in thin sections. Each dataset presented will serve as a benchmark to compare solutions to solve these problems, for instance using AI methods.​ ​ When ready, these benchmarks will be open to the geoscience and data science communities.​
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Dates et versions

hal-04104786 , version 1 (24-05-2023)

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  • HAL Id : hal-04104786 , version 1

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Nicolas Gilardi, Lisa Baulon, Hugo Breuillard, Musaab Khalid, Alexis Plunder, et al.. Geoscientific benchmarks for AI methods.. NuTS First Workshop, May 2023, Villeurbanne, France. . ⟨hal-04104786⟩

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