most citedVery high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar

358 citations · 358 across the 2 of their papers we have counts for

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cs.CV2026

CHMv2: Improvements in Global Canopy Height Mapping using DINOv3

John Brandt, Seungeun Yi, Jamie Tolan +9

Accurate canopy height information is essential for quantifying forest carbon, monitoring restoration and degradation, and assessing habitat structure, yet high-fidelity measuremen…

cs.CV2023★ 358 cited

Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar

Jamie Tolan, Hung-I Yang, Ben Nosarzewski +13

Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. Repeated measurement…