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

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

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

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

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

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.