metadata

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:

Reference AGB measurements were collected using LiDAR (Light Detection and Ranging) calibrated with in-situ measurements.
Total 13000 patches, each patch covering 2,560 by 2,560 meter area.

Feature data:

Sentinel-1 SAR and Sentinel-2 MSI data
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}
}