用遥感数据结合深度学习，预测法国南部葡萄种植潜力。
Predicting Viticulture Potential through an Ensemble of U-Net and a Geospatial Foundation Model
- 融合U-Net与地理空间基础模型，提升种植潜力预测精度。
- 在竞赛中达到68.32%准确率，排名第二。
- 适合农业规划与遥感应用研究者参考。
确定农业潜力是可持续土地管理和农业规划的基础。传统方法（如实地调查、土壤测试）成本高昂，遥感数据因此成为重要替代方案。ImageCLEF AI4Agri 2026：子任务1关注法国南部葡萄种植潜力的预测。DS@GT ARC团队提交的方案采用U-Net与地理空间基础模型Prithvi-2.0的集成方法。最佳模型在排行榜上取得68.32%的准确率，7支参赛队伍中位列第2。相关代码已公开于https://github.com/dsgt-arc/imageclef-ai4agri-2026。
原文摘要 · Abstract (English)
Determining agricultural potential is fundamental to sustainable land management and agricultural planning. Remote sensing data is increasingly valuable as an avenue for agricultural potential due to the cost of traditional methods (surveys, in-situ measurements, soil testing, etc). ImageCLEF AI4Agri 2026: Subtask 1 is concerned with the prediction of viticulture potential in Southern France. The DS@GT ARC's submission for Subtask 1 introduces an ensemble of U-Net and a Geospatial Foundation Model (Prithvi-2.0). Our best model achieved a $\pm$1 accuracy of 68.32 on the leaderboard, ranking 2nd among 7 teams. The implementation for this work is publicly available at https://github.com/dsgt-arc/imageclef-ai4agri-2026 .
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