geemap_docker
This is an Ubuntu Image that includes the geemap package and more that are used in GIS applications. Mostly designed for research purposes. You can find the full package list below:
light tag:
jupyter
jupyterlab
numpy
pandas
scipy
Pillow
matplotlib
folium
fiona
shapely
geopandas
rasterio
tifffile
geemap
latest tag: (+)
opencv-contrib-python
scikit-image
scikit-learn
sentinelhub
sentinelsat
xarray
whitebox
Tutorial
I highly recommend this tutorial that explains almost everything you need and even more. Please give an hour to understand how Docker works. You'll save a lot of time later:
KodeKloud FreeCodeCamp Docker Beginner Tutorial
Requirements
- You need successfully install Docker and run a container as the tutorial explains.
- If you operate on Windows, you need to enable Hyper-V since this is an Ubuntu container. Check here: Ubuntu Container on Windows
- You can now also use WSL 2 on Windows. Check here: Docker for WSL 2
Installation
Pull the image:
docker pull bkavlak/geemap:latest
And, see you have it in your local machine:
docker image ls
Process
Run the container by typing on bash or PowerShell:
docker run -it --name 'CONTAINER NAME' -p 8888:8888 -p 6006:6006 -v 'VOLUME DIRECTORY':/geemap/data bkavlak/geemap:latest bash
You should change text inside '___' as your preference (Don't include apostrophes ' ')
I highly recommend you add a volume to the container where you can transfer files between the computer and the container. (VOLUME DIRECTORY = where you put your files on the local machine) Without a volume, you'll lose the files after you stop the container. The volume helps you to create a bridge so that you can use any data on the VOLUME DIRECTORY.
After running the image you'll see a command as below:
root@00dda2cb5ece:/geemap#
This means that you are in the container.
To start a Jupyter Notebook:
jupyter notebook --ip=0.0.0.0 --port=8888 --allow-root
If successful, you will see some links below.
http://127.0.0.1:8888/?token......
Copy the last link to a browser (like Chrome). Hit ENTER.
See that you have a file data, which is the bridge between the container and local machine. You can move your data or files that you work on to the VOLUME DIRECTORY and see them on data in Jupyter.
If you have problems, it may connect to the Jupyter on your local. In this case try:
jupyter notebook --ip=0.0.0.0 --port=6006 --allow-root
If successful, you will see some links below.
http://127.0.0.1:6006/?token......
Finish
After you are done with the analyses, you can click COMMAND + C on bash to stop Jupyter and COMMAND + D to stop the container.
You can see the status of the container as:
docker ps -a
I recommend you remove the exited container:
docker rm -f 'CONTAINER ID'