This repository is archived. The model used in this research has been re-written in PyTorch and is now maintained as part of the DeepForest package. Please visit that repository to build on this work. Given the speed of development in deep learning and computer vision packages this code is now unlikely to run without the specific package versions from 2019.

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

Ben. G. Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan White

Summary

DeepLidar is a keras retinanet implementation for predicting individual tree crowns in RGB imagery.

How can I train new data?

DeepLidar uses a semi-supervised framework for model training. For generating lidar-derived training data see (). I recommend using a conda environments to manage python dependencies.

1. Create conda environment and install dependencies

conda env create --name DeepForest -f=generic_environment.yml

Clone the fork of the retinanet repo and install in local environment

conda activate DeepForest
git clone https://github.com/bw4sz/keras-retinanet
cd keras-retinanet
pip install .

2. Update config paths

All paths are hard coded into _config.yml

3. Train new model with new hand annotations

python train.py --retrain

How can I use pre-built models to predict new images.

Check out a demo ipython notebook: https://github.com/weecology/DeepLidar/tree/master/demo

Where are the data?

The Neon Trees Benchmark dataset is soon to be published. All are welcome to use it. Currently under curation (in progress): https://github.com/weecology/NeonTreeEvaluation/

For a static version of the dataset that reflects annotations at the time of submission, see dropbox link here

Published articles

Our first article was published in Remote Sensing and can be found here.

This codebase is constantly evolving and improving. To access the code at the time of publication, see Releases. The results of the full model can be found on our comet page.