构建首个10米分辨率国家尺度篱笆分割基准，用于评估遥感模型泛化能力。
Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark
- 整合多源遥感与法国实地数据，构建统一篱笆分割基准
- 在跨区域、跨气候区条件下测试模型泛化性能，验证真实场景适应性
- 支持监督与自监督学习方法，适合农业遥感与细粒度目标检测研究者
我们提出Hedgementation：一个用于评估机器学习模型在国家尺度和10平方米空间分辨率下从遥感数据中进行篱笆地图绘制的新基准。通过整合并协调多个遥感数据产品与来自法国篱笆普查的地面真值标签，构建统一数据集。评估三种基线模型在跨空间距离与跨气候区条件下的泛化能力，这是一个更具挑战性的任务。该基准同时测试了监督与自监督学习方法在追踪高农业价值的细粒度特征中的应用。基准及基线结果代码已公开于https://github.com/hedgementation/hedgementation。
原文摘要 · Abstract (English)
We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m$^2$ spatial resolution. We combine and harmonize multiple remote sensing data products and ground truth labels sourced from a hedgerow inventory in France. We measure the ability of three baseline models to generalize across spatial distance, and across climatic zones, a more explicitly challenging task. Our benchmark tests both supervised and self-supervised learning approaches for remote sensing, applied to tracking fine-scale features of high agricultural importance. The code to reproduce the benchmark and baselines results is available at https://github.com/hedgementation/hedgementation.
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