AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials
Not on the map. Why?
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
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
12 of 39 references.
References · 12
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
- 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 408
- 2023SpectralGPT: Spectral Remote Sensing Foundation Modelcited by 252
- 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 234
- 2021Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networkscited by 143
- 2021Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Seriescited by 49
- 2019A CNN-RNN Framework for Crop Yield Predictioncited by 36
- 2019Crop Yield Prediction Using Deep Neural Networkscited by 29
- 2022A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningcited by 19
- 2024An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictionscited by 11
- 2020Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approachcited by 6
Built on by
Citing papers per year: 2026: 2.
2 papers.