Python package for training and predicting individual tree crowns in airborne imagery.
git clone https://github.com/weecology/DeepForest.git
This package depends on keras-retinainet for object detection.
git clone https://github.com/fizyr/keras-retinanet.git
cd keras-retinanet
pip install .
python setup.py build_ext --inplace
DeepForest uses conda as a packgae manager.
conda env create --file=environment.yml
from deepforest import deepforest
from deepforest import utilities
#Download latest model release from github
utilities.use_release()
#Load model class with release weights
test_model = deepforest.deepforest(weights="data/universal_model_july30.h5")
#predict image
image = test_model.predict_image(image_path = "tests/data/OSBS_029.tif")
Thanks to Microsoft AI4Earth grant for hosting a azure web demo of the trained model.
http://tree.westus.cloudapp.azure.com/shiny/
- Free software: MIT license
- Documentation: https://deepforest.readthedocs.io.
Geographic Generalization in Airborne RGB Deep Learning Tree Detection Ben Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan P White bioRxiv 790071; doi: https://doi.org/10.1101/790071
We are organizing a benchmark dataset for individual tree crown prediction in RGB imagery from the National Ecological Observation