Sub-meter resolution canopy height maps using self-supervised learning and a vision transformer trained on Aerial and GEDI Lidar. (arXiv:2304.07213v1 [cs.CV])

Vegetation structure mapping is critical for understanding the global carbon
cycle and monitoring nature-based approaches to climate adaptation and
mitigation. Repeat measurements of these data allow for the observation of
deforestation or degradation of existing forests, natural forest regeneration,
and the implementation of sustainable agricultural practices like agroforestry.
Assessments of tree canopy height and crown projected area at a high spatial
resolution are also important for monitoring carbon fluxes and assessing
tree-based land uses, since forest structures can be highly …

arxiv carbon climate data deforestation global implementation lidar mapping maps monitoring natural nature observation practices self-supervised learning supervised learning transformer tree understanding vision