This code was prepared by:
- Di Chen (Cornell University, Dept. of Computer Science), dc874@cornell.edu
- Yiwei Bai (Cornell University, Dept. of Computer Science), yb263@cornell.edu
- Daniel Fink (Cornell University, Laboratory of Ornithology), daniel.fink@cornell.edu
- Carla P. Gomes (Cornell University, Dept. of Computer Science), gomes@cs.cornell.edu
This code accompanies the following publication:
- Courtney L. Davis, Yiwei Bai, Di Chen, Orin Robinson, Viviana R. Gutierrez, Carla P. Gomes, and Daniel Fink. Deep learning with citizen science data enables estimation of species diversity and composition at continental extents. Accepted at Ecology.
Please see the LICENSE file for additional terms of use, and for information on acquiring data for other purposes.
This repository is archived on Zenodo 
- Python 3.7
- PyTorch 0.4.1
- TensorFlow 1.8
- Torchvision 0.4.0
- Scikit-image
- Scikit-learn 0.19.2
- R 3.6.1
- Hmsc R library (see https://github.com/hmsc-r/HMSC)
Helper scripts also assume a Bourne or Bash shell, however they are short and can easily be converted to other platforms or entered manually.
More information about each application and dataset is included in the readme.md files in each subdirectory. The scripts and instructions for replicating the results from each experiment in the publication are in these subdirectories:
- BBS : experiments using the 2011 Breeding Bird Survey (BBS) dataset
- eBird_entire : experiments using the entire eBird dataset to showcase the ecological utility
- eBird_subsets : experiments using the subsets of eBird dataset with different scales
- Norberg2019 : experiments using the 5 species datasets from Norbeg et. al 2019