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树冠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，该分辨率下开放数据集的缺乏阻碍了模型的可复现性与评估。我们提出了Open-Canopy，首个面向超高分辨率（1.5米）树冠高度估算的开放获取、国家尺度基准数据集，其覆盖法国超过87,000平方公里区域，包含1.5米分辨率卫星影像与航空激光雷达（LiDAR）数据。此外，我们还推出了Open-Canopy-$\Delta$，这是一个用于不同年份图像间树冠高度变化检测（至单树级别）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模型架构，揭示了当前面临的重大挑战与改进机遇。我们的数据集与代码已在https://github.com/fajwel/Open-Canopy公开提供。