Canopy Height

Canopy height estimation is crucial for understanding forest biomass, biodiversity, and carbon cycling, driving research into accurate and efficient measurement techniques. Current efforts focus on leveraging diverse data sources, including LiDAR, satellite imagery (optical and SAR), and drone-based sensors, combined with advanced machine learning models like U-Nets, Vision Transformers, and other deep learning architectures, to generate high-resolution canopy height maps at various scales. These advancements improve the accuracy and efficiency of global-scale vegetation monitoring, supporting applications in climate change mitigation, forest management, and ecosystem conservation. The development of open-access datasets and benchmark models is fostering collaboration and accelerating progress in the field.