  Accurate estimates of Above Ground Biomass (AGB) are essential in addressing
two of humanity's biggest challenges, climate change and biodiversity loss.
Existing datasets for AGB estimation from satellite imagery are limited. Either
they focus on specific, local regions at high resolution, or they offer global
coverage at low resolution. There is a need for a machine learning-ready,
globally representative, high-resolution benchmark. Our findings indicate
significant variability in biomass estimates across different vegetation types,
emphasizing the necessity for a dataset that accurately captures global
diversity. To address these gaps, we introduce a comprehensive new dataset that
is globally distributed, covers a range of vegetation types, and spans several
years. This dataset combines AGB reference data from the GEDI mission with data
from Sentinel-2 and PALSAR-2 imagery. Additionally, it includes pre-processed
high-level features such as a dense canopy height map, an elevation map, and a
land-cover classification map. We also produce a dense, high-resolution (10m)
map of AGB predictions for the entire area covered by the dataset. Rigorously
tested, our dataset is accompanied by several benchmark models and is publicly
available. It can be easily accessed using a single line of code, offering a
solid basis for efforts towards global AGB estimation. The GitHub repository
github.com/ghjuliasialelli/AGBD serves as a one-stop shop for all code and
data.
