--- 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