Open-Canopy: Towards Very High Resolution Forest Monitoring
This addresses the problem of limited open-access data for high-resolution forest monitoring, enabling reproducible research in environmental computer vision, though it is incremental as it focuses on dataset creation rather than novel methods.
The authors tackled the challenge of estimating and monitoring forest canopy height at very high resolution by introducing Open-Canopy, the first open-access, country-scale benchmark dataset covering over 87,000 km² in France with 1.5 m resolution satellite imagery and LiDAR data, and Open-Canopy-Δ for change detection, evaluating state-of-the-art models to reveal significant challenges.
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-$Δ$, 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.