Estimating canopy height and canopy height change at meter resolution from satellite imagery has numerous applications, such as monitoring forest health, logging activities, wood resources, and carbon stocks. However, many existing forest datasets are based on commercial or closed data sources, restricting the reproducibility and evaluation of new approaches. To address this gap, we introduce Open-Canopy, the first open-access and country-scale benchmark for very high resolution (1.5 m) canopy height estimation. Covering more than 87,000 km$^2$ across France, Open-Canopy combines SPOT satellite imagery with high resolution aerial LiDAR data. We also propose Open-Canopy-$\Delta$, the first benchmark for canopy height change detection between two images taken at different years, a particularly challenging task even for recent models. To establish a robust foundation for these benchmarks, we evaluate a comprehensive list of state-of-the-art computer vision models for canopy height estimation. The dataset and associated codes can be accessed at https://github.com/fajwel/Open-Canopy.

翻译：利用卫星影像以米级分辨率估算冠层高度及其变化具有广泛的应用价值，例如监测森林健康状况、采伐活动、木材资源及碳储量。然而，现有森林数据集多基于商业或封闭数据源，限制了新方法的可复现性与评估。为填补这一空白，我们提出了Open-Canopy——首个面向超高分辨率（1.5米）冠层高度估算的开放获取国家尺度基准数据集。该数据集覆盖法国超过87,000平方公里区域，融合了SPOT卫星影像与高分辨率航空激光雷达数据。同时，我们推出了Open-Canopy-Δ——首个针对不同年份双时相影像的冠层高度变化检测基准，该任务即使对最新模型也极具挑战性。为构建坚实的评估基础，我们对当前最先进的计算机视觉冠层高度估算模型进行了系统性评估。数据集及相关代码可通过https://github.com/fajwel/Open-Canopy获取。