***4 nov 2025*** *** NEWS: the agripotential dataset will be part of ImageCLEF 2026 competition. Please contact us if you're interested in participating *** *** NEWS: an extended version of the dataset will be soon released, please get in touch if you're interested as we can provide you with a pre-release! *** ***UPDATE: this work was accepted in the conference IEEE CBMI 2025 that will be held in dublin in late october 2025. A final peer-reviewed version will be published soon*** This is the official page for AgriPotential, a publicly available benchmark dataset designed for assessing agricultural potentials using remote sensing data. It integrates 11 Sentinel-2 satellite images from 2019 across 10 spectral bands, covering agricultural regions in the Hérault Department, Southern France. The dataset includes pixel-wise labels representing agricultural potential levels (from Very Low to Very High) across three crop types: viticulture, market gardening, and field crops. Ground truth annotations are derived from the BD Sol - GDPA database and validated by domain experts. The dataset is stored in HDF5 format and supports a range of machine learning tasks, including ordinal classification, regression, segmentation, and spatio-temporal modeling. This dataset facilitates scalable, data-driven solutions for land suitability analysis, agricultural planning, and sustainable resource management. On this page you will find a PDF with supplmentary results for the paper. A tutorial GitHub is also available: https://github.com/MohammadElSakka/agripotential-dataset Raw data that was used to make this dataset can be found here (there isn't much to do there apart from downloading it): https://zenodo.org/records/15551802