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---
license: mit
language:
- en
---
## Tree Crown Detection in RGB Airborne Imagery
The model was initially described in [Remote Sensing](https://www.mdpi.com/2072-4292/11/11/1309) on a single site. The prebuilt model uses a semi-supervised approach in which millions of moderate quality annotations are generated using a LiDAR unsupervised tree detection algorithm, followed by hand-annotations of RGB imagery from select sites. Comparisons among geographic sites were added to [Ecological Informatics](https://www.sciencedirect.com/science/article/pii/S157495412030011X). The model was further improved, and the Python package was released in [Methods in Ecology and Evolution](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13472).
- **Repository:** https://github.com/weecology/DeepForest
- **Paper :** https://www.sciencedirect.com/science/article/pii/S157495412030011X
- **Demo :** https://huggingface.co/spaces/weecology/deepforest-demo
## How to Get Started with the Model
Use the code below to get started with the model.
```
from deepforest import main
from deepforest import get_data
from deepforest.visualize import plot_results
# Initialize the model class
model = main.deepforest()
# Load a pretrained tree detection model from Hugging Face
model.load_model(model_name="weecology/deepforest-tree", revision="main")
sample_image_path = get_data("OSBS_029.png")
img = model.predict_image(path=sample_image_path)
plot_results(img)
```
## Citation
```
@article{WEINSTEIN2020101061,
title = {Cross-site learning in deep learning RGB tree crown detection},
journal = {Ecological Informatics},
volume = {56},
pages = {101061},
year = {2020},
issn = {1574-9541},
doi = {https://doi.org/10.1016/j.ecoinf.2020.101061},
url = {https://www.sciencedirect.com/science/article/pii/S157495412030011X},
author = {Ben G. Weinstein and Sergio Marconi and Stephanie A. Bohlman and Alina Zare and Ethan P. White},
```
## Model Card Authors
Ben G. Weinstein, Sergio Marconi, Stephanie A. Bohlman, Alina Zare and Ethan P. White |