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

TL;DR

AgriPotential introduces a comprehensive multi-spectral, multi-temporal satellite dataset with pixel-level annotations for agricultural potential prediction, supporting diverse machine learning tasks to enhance sustainable land management.

Contribution

It provides the first public dataset specifically designed for agricultural potential prediction using satellite imagery, enabling advanced machine learning research in this domain.

Findings

Dataset covers diverse regions in Southern France.

Supports multiple machine learning tasks including ordinal regression and multi-label classification.

Facilitates improved data-driven land use planning.

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 captured over 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 cover 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/15551829