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

arXiv: https://arxiv.org/abs/2506.11740 · HTML: https://mlsanity.com/paper/2506.11740/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series](https://mlsanity.com/paper/2112.07558/) (2021)
- [A CNN-RNN Framework for Crop Yield Prediction](https://mlsanity.com/paper/1911.09045/) (2019)
- [Crop Yield Prediction Using Deep Neural Networks](https://mlsanity.com/paper/1902.02860/) (2019)
- [A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning](https://mlsanity.com/paper/2204.00951/) (2022)
- [An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions](https://mlsanity.com/paper/2406.06081/) (2024)
- [Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach](https://mlsanity.com/paper/2001.09902/) (2020)

## Built on by (most cited first, 2 of 2)

- [SeDa: A Unified System for Dataset Discovery and Multi-Entity Augmented Semantic Exploration](https://mlsanity.com/paper/2603.07502/) (2026)
- [Predicting Viticulture Potential through an Ensemble of U-Net and a Geospatial Foundation Model](https://mlsanity.com/paper/2607.08449/) (2026)

## Abstract (arXiv)

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