AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials

• URL: http://arxiv.org/abs/2506.11740v1
• Date: Fri, 13 Jun 2025 12:52:46 GMT
• Title: AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials
• Authors: Mohammad El Sakka, Caroline De Pourtales, Lotfi Chaari, Josiane Mothe,
• Abstract summary: We introduce AgriPotential, a novel benchmark dataset composed of Sentinel-2 satellite imagery spanning multiple months.<n>The dataset provides pixel-level annotations of agricultural potentials for three major crop types.<n>The data covers diverse areas in Southern France, offering rich spectral information.
• Score: 2.231167375820084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote sensing has emerged as a critical tool for large-scale Earth monitoring and land management. In this paper, we introduce AgriPotential, a novel benchmark dataset composed of Sentinel-2 satellite imagery spanning multiple months. The dataset provides pixel-level annotations of agricultural potentials for three major crop types - viticulture, market gardening, and field crops - across five ordinal classes. AgriPotential supports a broad range of machine learning tasks, including ordinal regression, multi-label classification, and spatio-temporal modeling. The data covers diverse areas in Southern France, offering rich spectral information. AgriPotential is the first public dataset designed specifically for agricultural potential prediction, aiming to improve data-driven approaches to sustainable land use planning. The dataset and the code are freely accessible at: https://zenodo.org/records/15556484

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