AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials
This dataset addresses the need for public benchmarks in agricultural remote sensing to enhance sustainable land use planning, though it is incremental as it builds on existing satellite data.
The authors introduced AgriPotential, a novel multi-spectral and temporal remote sensing dataset for agricultural potential prediction, covering three crop types across five ordinal classes in Southern France to support machine learning tasks like ordinal regression and spatio-temporal modeling.
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