# AGBD: A Global-scale Biomass Dataset

arXiv: https://arxiv.org/abs/2406.04928 · HTML: https://mlsanity.com/paper/2406.04928/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar](https://mlsanity.com/paper/2304.07213/) (2023)
- [Estimating Canopy Height at Scale](https://mlsanity.com/paper/2406.01076/) (2024)
- [High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2](https://mlsanity.com/paper/2311.14006/) (2023)

## Built on by (most cited first, 13 of 13)

- [REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation](https://mlsanity.com/paper/2412.16583/) (2024)
- [NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation](https://mlsanity.com/paper/2510.17914/) (2025)
- [MMEarth-Bench: Global Model Adaptation via Multimodal Test-Time Training](https://mlsanity.com/paper/2602.06285/) (2026)
- [FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping](https://mlsanity.com/paper/2506.07080/) (2025)
- [SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in Ecology](https://mlsanity.com/paper/2504.18256/) (2025)
- [GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance](https://mlsanity.com/paper/2504.01722/) (2025)
- [A comprehensive GeoAI review: Progress, Challenges and Outlooks](https://mlsanity.com/paper/2412.11643/) (2024)
- [Towards Unified Vision Language Models for Forest Ecological Analysis in Earth Observation](https://mlsanity.com/paper/2511.16853/) (2025)
- [Validating remotely sensed biomass estimates with forest inventory data in the western US](https://mlsanity.com/paper/2506.03120/) (2025)
- [Learning to Predict Aboveground Biomass from RGB Images with 3D Synthetic Scenes](https://mlsanity.com/paper/2511.23249/) (2025)
- [Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance](https://mlsanity.com/paper/2604.03874/) (2026)
- [Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin](https://mlsanity.com/paper/2606.05368/) (2026)
- [Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach](https://mlsanity.com/paper/2606.07249/) (2026)

## Abstract (arXiv)

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.