Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However, the lack of open-access datasets at this resolution hinders the reproducibility and evaluation of models. We introduce Open-Canopy, the first open-access, country-scale benchmark for very high-resolution (1.5 m) canopy height estimation, covering over 87,000 km$^2$ across France with 1.5 m resolution satellite imagery and aerial LiDAR data. Additionally, we present Open-Canopy-$\Delta$, a benchmark for canopy height change detection between images from different years at tree level-a challenging task for current computer vision models. We evaluate state-of-the-art architectures on these benchmarks, highlighting significant challenges and opportunities for improvement. Our datasets and code are publicly available at https://github.com/fajwel/Open-Canopy.

翻译：从卫星图像以米级分辨率估算冠层高度及其变化是计算机视觉领域的一项重大挑战，具有关键的环境应用价值。然而，由于缺乏该分辨率的开放访问数据集，模型的复现与评估受到阻碍。我们推出了Open-Canopy，这是首个面向超高分辨率（1.5米）冠层高度估算的开放访问、国家尺度基准数据集，覆盖法国超过87,000平方公里区域，包含1.5米分辨率卫星影像与航空LiDAR数据。此外，我们还提出了Open-Canopy-$\Delta$，这是一个用于检测不同年份图像间树冠高度变化的基准数据集——这对当前计算机视觉模型而言是一项极具挑战性的任务。我们在这些基准上评估了最先进的架构，揭示了显著的挑战与改进机遇。我们的数据集与代码已在https://github.com/fajwel/Open-Canopy 公开提供。