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---
license: apache-2.0
task_categories:
- image-segmentation
- image-feature-extraction
language:
- en
tags:
- climate
pretty_name: d
size_categories:
- 100M<n<1B
---
# Manual crown delineation of individual trees in 2 countries: Denmark and Finland
Dataset download link: https://sid.erda.dk/sharelink/eFt21tspNe
GitHub page: https://github.com/sizhuoli/TreeCountSegHeight
Publication: https://doi.org/10.1093/pnasnexus/pgad076
----------------------------------------
## Dataset description
---------- **Denmark data**---------- :
More than 20k individual trees growing in different landscapes.
Annotation masks are paired with aerial photographs with a spatial resolution of 20cm.
Images contain RGB and near-infrared bands and were taken in summer 2018.
Image credits: Danish Agency for Data Supply and Infrastructure (https://sdfi.dk/)
---------- **Finland data**---------:
More than 4k individual tree crowns in random sampling regions
Annotation masks are paired with aerial photographs containing near-infrared, green, and blue bands with a spatial resolution of 50cm
Image credits: National Survey of Finland (https://www.maanmittauslaitos.fi/en/maps-and-spatial-data)
## Tasks
* Segmentation of individual tree crowns
* Transfer learning/domain adaptation between datasets with different visual semantics, band compositions, forest species, etc.
## Citation
```
@article{li2023deep,
title={Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale},
SHORTauthor={Li, Sizhuo and Brandt, Martin and Fensholt, Rasmus and Kariryaa, Ankit and Igel, Christian and Gieseke, Fabian and Nord-Larsen, Thomas and Oehmcke, Stefan and Carlsen, Ask Holm and Junttila, Samuli and others},
author={Li, Sizhuo and Brandt, Martin and Fensholt, Rasmus and Kariryaa, Ankit and Igel, Christian and Gieseke, Fabian and Nord-Larsen, Thomas and Oehmcke, Stefan and Carlsen, Ask Holm and Junttila, Samuli and Xiaoye Tong and Alexandre d’Aspremont and Philippe Ciais},
journal={PNAS nexus},
volume={2},
number={4},
year={2023},
publisher={Oxford University Press}
}
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