Geoscientific benchmarks for AI methods.
Overview & Synopsis
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.
Indexed from repository: HAL Open Science
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hal.science)
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Document Information
| Document Title | Geoscientific benchmarks for AI methods. |
|---|---|
| Author / Organization | Nicolas Gilardi, Lisa Baulon, Hugo Breuillard, Musaab Khalid, Alexis Plunder, Alex Vella |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2023 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-122090 |
| Original Source Link | Visit Source Page on hal.science » |