| Description |
Above Ground Biomass is an important variable as forests play a crucial role in mitigating climate change as they act as an efficient, natural and cost-effective carbon sink. Traditional field and airborne LiDAR measurements have been proven to provide reliable estimations of forest biomass. Nevertheless, the use of these techniques at a large scale can be challenging and expensive. Satellite data have been widely used as a valuable tool in estimating biomass on a global scale. However, the full potential of dense multi-modal satellite time series, in combination with modern deep learning approaches, has yet to be fully explored. The aim of the BioMassters data challenge and benchmark dataset is to investigate the potential of multi-modal Sentinel-1 SAR and Sentinel-2 MSI satellite data to estimate forest biomass at a large scale using the Finnish Forest Centre's open forest and nature airborne LiDAR data as a reference. DrivenData hosted a machine-learning competition to estimate Above-Ground Biomass (AGB) in the forests of Finland. The performance of the top-three baseline models shows the potential of these techniques to produce accurate and higher-resolution biomass maps. |
| Folder |
/datasets/geoai/ibm-nasa-geospatial/BioMassters |
| Discipline |
GeoAI / Remote Sensing / Climate Science |
| DOI |
10.57967/hf/1009 |
| Link |
Access Data |
| Public |
True |
| Publication Date |
2024-01-15 |
| Downloaded |
2025-09-10 |
| Data Type |
GeoTIFF |
| Dataset Size |
188G |
| Number of Files |
98 |
| Usage |
$ module avail
$ module load datasets
$ module load geoai/ibm-nasa-geospatial/BioMassters/2024-01-15 |
| Usage Policy Link |
https://choosealicense.com/licenses/cc-by-sa-4.0/ |
| Usage Policy |
This dataset is released under the Creative Commons Attribution–ShareAlike 4.0 International (CC BY-SA 4.0) license. You may use, adapt, and build upon the data for research, educational, and commercial purposes, provided that appropriate credit is given to the original authors and source materials, including citation of the associated paper and DOI. Any derivative works must be shared under the same CC BY-SA 4.0 terms, and users must clearly indicate if changes were made. Use of this dataset is subject to applicable legal and ethical guidelines, and no warranty is provided. Please consult the LICENSE file or official license page for full license terms. |
| Citation |
Nascetti, A., Yadav, R., Brodt, K., Qu, Q., Fan, H., Shendryk, Y., Shah, I., & Chung, C. (2023). BioMassters: A benchmark dataset for forest biomass estimation using multi-modal satellite time-series. In NeurIPS 2023 Datasets and Benchmarks Track. Retrieved from https://openreview.net/forum?id=hrWsIC4Cmz |
| BibTeX |
đź“ś View BibTeX citation@inproceedings{
nascetti2023biomassters,
title={BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series},
author={Andrea Nascetti and RITU YADAV and Kirill Brodt and Qixun Qu and Hongwei Fan and Yuri Shendryk and Isha Shah and Christine Chung},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=hrWsIC4Cmz}
} |