Python scripts and Jupyter Notebooks for the textbook Image Analysis, Classification and Change Detection in Remote Sensing, Fifth Revised Edition included in the Docker image
mort/crc5docker
The scripts are documented in
python_scripts.pdf
Pull and/or run the container for the first time with
docker run -d -p 8888:8888 -v <path-to-crc5imagery>:/home/imagery/ --name=crc5 mort/crc5docker
This maps the host directory crc5imagery to the container directory /home/imagery/ and runs the container in detached mode. The compressed crc5imagery directory can be downloaded from
https://drive.google.com/file/d/1EOJolX0Diumo0ebM6xDvCZQqb34s8Gxz/view?usp=sharing
Point your browser to http://localhost:8888 to see the JupyterLab home page and open a Chapter notebook.
Stop the container with
docker stop crc5
Re-start with
docker start crc5
In order to be able to access the Google Earth Engine from within the Chapter notebooks you can register a free, non-commercial Google Cloud project if you have not already done so:
https://earthengine.google.com/
For LLM enthusiasts an experimental RAG (retrieval augmented generation) version of the Docker container can be pulled and run with
docker run -d -p 8888:8888 -p 7860-7869:7860-7869 -v <path-to-crc5imagery>:/home/imagery/ --name=crc5_rag mort/crc5docker_rag
which includes an additional JupyterLab notebook to query the textbook's content informally. If the LLM is running locally on a CPU, the response time is very slow (minutes). and the generated answers are often misleading. If you use a cloud version (see the RAG Notebook), response time is in seconds and the answers are much more reliable and pertinent. A (free) ollama account is required.
Book Summary
chapter_abstracts.pdf
Additional Earth Engine Tutorials
https://developers.google.com/earth-engine/tutorials/community/imad-tutorial-pt1
https://developers.google.com/earth-engine/tutorials/community/imad-tutorial-pt2
https://developers.google.com/earth-engine/tutorials/community/imad-tutorial-pt3