Dataset Open Access
TreeSatAI Benchmark Archive for Deep Learning in Forest Applications
Schulz, Christian;
Ahlswede, Steve;
Gava, Christiano;
Helber, Patrick;
Bischke, Benjamin;
Arias, Florencia;
Förster, Michael;
Hees, Jörn;
Demir, Begüm;
Kleinschmit, Birgit
BibTeX Export
@dataset{schulz_christian_2022_6598391,
author = {Schulz, Christian and
Ahlswede, Steve and
Gava, Christiano and
Helber, Patrick and
Bischke, Benjamin and
Arias, Florencia and
Förster, Michael and
Hees, Jörn and
Demir, Begüm and
Kleinschmit, Birgit},
title = {{TreeSatAI Benchmark Archive for Deep Learning in
Forest Applications}},
month = aug,
year = 2022,
note = {{TreeSatAI was funded by the Bundesministerium für
Bildung und Forschung (BMBF) under the grant
number 01IS20014A. We are grateful to the
Niedersächsische Landesforsten for providing the
forest management data, the inventory data and the
aerial imagery.}},
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.6598391},
url = {https://doi.org/10.5281/zenodo.6598391}
}
2,275
4,770
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 2,275 | 1,131 |
| Downloads | 4,770 | 1,942 |
| Data volume | 1.6 TB | 619.0 GB |
| Unique views | 1,829 | 1,008 |
| Unique downloads | 2,195 | 1,033 |
Indexed in
- Publication date:
- August 1, 2022
- DOI:
-
- Keyword(s):
- deep learning forest tree image classification remote sensing aerial imagery species mapping sentinel-2 sentinel-1 multi-modal fusion artificial intelligence lower saxony forest inventory mid-Europe
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International