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---
license: cc-by-4.0
language:
- en
tags:
- climate
pretty_name: BioMassters
size_categories:
- 100K<n<1M
---
# BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series https://nascetti-a.github.io/BioMasster/
The objective of this repository is to provide a deep learning ready dataset to predict yearly Above Ground Biomass (AGB) for Finnish forests using multi-temporal satellite imagery from
the European Space Agency and European Commission's joint Sentinel-1 and Sentinel-2 satellite missions, designed to collect a rich array of Earth observation data
### Reference data:
* pixel-wise above-ground biomass maps
* Measurements were collected using LiDAR calibrated with in-situ measurements.
* Total 13000 patches, each patch covering 2,560 X 2,560 meter area.
### Feature data:
* Sentinel-1 SAR (band order: ASC VV, ASC VH, DSC VV, DSC VH)
* Sentinel-2 MSI (band order: B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12, CLP)
* 12 months of data (1 image per month)
* Total 310,000 patches
### Data Specifications:
### Data Size:
```
dataset | # files | size
--------------------------------------
train_features | 189078 | 215.9GB
test_features | 63348 | 73.0GB
train_agbm | 8689 | 2.1GB
```
## Citation:
```
@inproceedings{nascetti2023biomassters,
title={BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series},
author={Nascetti, Andrea and Yadav, Ritu and Brodt, Kirill and Qu, Qixun and Fan, Hongwei and Shendryk, Yuri and Shah, Isha and Chung, Christine},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023}
}
```
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