首个面向农业潜力预测的多光谱时序遥感数据集
AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials
- 基于哨兵2号卫星多月影像构建，覆盖法国南部
- 提供三大作物的像素级潜力标注，分5个等级
- 适合做土地可持续规划与机器学习研究
遥感已成为大范围地球监测与土地管理的关键工具。本文介绍AgriPotential，一个由多月哨兵2号卫星影像组成的新型基准数据集。该数据集包含葡萄种植、园艺和大田作物三类主要作物的像素级农业潜力标注，共五个有序类别。AgriPotential支持有序回归、多标签分类和时空建模等多种机器学习任务，覆盖法国南部多个区域，具备丰富的光谱信息。这是首个专为农业潜力预测设计的公开数据集，旨在提升数据驱动的可持续土地利用规划能力。数据集与代码可免费获取：https://zenodo.org/records/15551829
原文摘要 · Abstract (English)
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
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