DeepForest

Python package for training and predicting individual tree crowns in airborne imagery.

Installation

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

Python dependencies

DeepForest uses conda as a packgae manager.

conda env create --file=environment.yml

Usage

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")

Web Demo

Thanks to Microsoft AI4Earth grant for hosting a azure web demo of the trained model.

http://tree.westus.cloudapp.azure.com/shiny/

License

Free software: MIT license
Documentation: https://deepforest.readthedocs.io.

Citation

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

Where can I get sample data?

We are organizing a benchmark dataset for individual tree crown prediction in RGB imagery from the National Ecological Observation

https://github.com/weecology/NeonTreeEvaluation

mrhelmus/DeepForest