  An increasing amount of companies and cities plan to become CO2-neutral,
which requires them to invest in renewable energies and carbon emission
offsetting solutions. One of the cheapest carbon offsetting solutions is
preventing deforestation in developing nations, a major contributor in global
greenhouse gas emissions. However, forest preservation projects historically
display an issue of trust and transparency, which drives companies to invest in
transparent, but expensive air carbon capture facilities. Preservation projects
could conduct accurate forest inventories (tree diameter, species, height etc.)
to transparently estimate the biomass and amount of stored carbon. However,
current rainforest inventories are too inaccurate, because they are often based
on a few expensive ground-based samples and/or low-resolution satellite
imagery. LiDAR-based solutions, used in US forests, are accurate, but
cost-prohibitive, and hardly-accessible in the Amazon rainforest. We propose
accurate and cheap forest inventory analyses through Deep Learning-based
processing of drone imagery. The more transparent estimation of stored carbon
will create higher transparency towards clients and thereby increase trust and
investment into forest preservation projects.
