Datasets:
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license: other
license_name: prosperity-public-license-3.0.0
license_link: LICENSE
task_categories:
- image-classification
- image-segmentation
- image-feature-extraction
language:
- en
---
# DeepTrees Halle DOP20 labels + imagery
[](https://doi.org/10.57967/hf/4213)
This is a sample dataset for model training and fine-tuning in tree crown segmentation tasks using the [DeepTrees](https://deeptrees.de) Python package.
Overview of subtiles with sample of labels:
## Dataset Details
We have taken a single Multispectral (RGBi) 2x2 km DOP20 image tile for Halle, Sachsen-Anhalt, from LVermGeo ST for the year of 2022.
**TileID from source:** 32_704_5708_2
We then sliced the tiles into subtiles of 100x100m, resulting in 400 subtiles. These are provided as 4-band raster `.tif` files.
We are manually labelling tree crowns in these subtiles based on an entropy-based active learning approach. The label classes have been provided below.
The labeling is done in QGIS and the polygon vectors are provided as ESRI-shape `.shp` files per subtile.
### Label Classes
**0** = tree
**1** = cluster of trees
**2** = unsure
**3** = dead trees (haven’t added yet)
### Dataset Description
- **Curated by:** Taimur Khan
- **Funded by:** Helmholtz Center for Environmental Research - UFZ
- **Contact:** [taimur.khan@ufz.de](mailto:taimur.khan@ufz.de)
### Cite the dataset
```
@misc {taimur_khan_2025,
author = { {Taimur Khan} },
title = { DeepTrees_Halle (Revision 0c528b9) },
year = 2025,
url = { https://huggingface.co/datasets/thisistaimur/DeepTrees_Halle },
doi = { 10.57967/hf/4213 },
publisher = { Hugging Face }
}
```
### License
This repository is made avaiable under The Prosperity Public License 3.0.0. A copy of the license can be found in the [LICENSE.md](LICENSE.md) file.
### References
- Halle DOP20: © [GeoBasis-DE / LVermGeo ST 2022](https://www.lvermgeo.sachsen-anhalt.de/de/gdp-digitale-orthophotos.html)
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