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:
- 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}
}