AGBD: A Global-scale Biomass Dataset
This provides a machine-learning-ready dataset for researchers and practitioners working on climate change and biodiversity loss, though it is incremental as it builds on existing satellite data.
The authors tackled the lack of a globally representative, high-resolution benchmark dataset for Above Ground Biomass (AGB) estimation by introducing a comprehensive new dataset that combines GEDI, Sentinel-2, and PALSAR-2 data with pre-processed features, resulting in a dense, high-resolution (10m) AGB prediction map for the entire covered area.
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 dataset. 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.