Source files for the Docker image mort/crc4docker
Python scripts for the textbook "Image Analysis, Classification and Change Detection in Remote Sensing, Fourth Revised Edition"
On Ubuntu, for example, pull and/or run the container for the first time with
sudo docker run -d -p 443:8888 -p 6006:6006 -v <my_image_folder>:/home/myimagery/ --name=crc4 mort/crc4docker
This maps the host directory my_image_folder to the container directory /home/myimagery/ and runs the container in detached mode.
If you have a Linux installation and one or more Nvidia graphics cards and wish to use TensorFlow with gpu acceleration install the latest Nvidia driver and the Nvidia runtime, see https://github.com/NVIDIA/nvidia-docker.
Then pull and run the gpu-enabled container with
sudo docker run --runtime=nvidia -d -p 443:8888 -p 6006:6006 -v <my_image_folder>:/home/myimagery/ --name=crc4 mort/crc4dockergpu
Point your browser to http://localhost:443 to see the Jupyter home page and open a notebook.
In order to use Earth Engine scripts, you must authenticate. From the home page open a local terminal and enter
earthengine authenticate
and follow the instrctions. This adds authentication information to the container and only has to be done once.
Stop the container with
sudo docker stop crc4
Re-start with
sudo docker start crc4