Rapid image labeling for data-driven Earth science discovery.
A tool for web-based image annotation and efficient labeling pixels in images
Implements a rapid technique, described by Buscombe & Ritchie, (2018), for dense image labeling based on limited manual annotations.
Credits: Thanks to code contributions from Colin Talbert and Rich Signell
Example instructional video here
First, if you're a regular conda user, you should start by doing some housekeeping:
conda clean --all
conda update anaconda
conda update conda
conda update --all
Clone the repository:
git clone https://github.com/dbuscombe-usgs/EarthAnnotator.git
conda config --remove channels conda-forge
Create a conda environment:
conda config --add channels conda-forge
conda env create -f binder\environment.yml
Activate the conda environment (called EA):
conda activate EA
conda config --remove channels conda-forge
Add the kernel to jupyter:
python -m ipykernel install --user --name EA --display-name "Python (earthannotator)"
Have a look at the kernels
jupyter kernelspec list
Run the notebook (shows up in your web browser):
jupyter notebook