Geospatial Data Cookbook

This cookbook deals with Geospatial Data Handling, specifically focusing on Open Source Geospatial Data like OpenStreetMap. It presents the different step of a pipeline that encompasses Data Acqusition, Feature Engineering, Segmentation and Visualisation.

Motivation¶

The goal is to guide users through the acquisition and preprocessing of geospatial data as a foundation for GeoAI and geospatial analysis workflows.

Authors¶

Sophie Haselsteiner (Author), Negar Alinaghi (Maintainer), Ioannis Giannopoulos (Maintainer)

Structure¶

Introduction and Fundamental Concepts
Data Acquisition
Feature Engineering
Grids and Segmentation
Visualisation

Running the Notebooks¶

Running on TU Cookbooks Binder¶

Go to TU Cookbooks Binder: https://binder.cookbooks.datalab.tuwien.ac.at/

Running on Your Own Machine¶

If you are interested in running this material locally on your computer, you will need to follow this workflow:

Clone the https://gitlab.tuwien.ac.at/cookbooks/public/geospatial_data repository:
```
 git clone https://gitlab.tuwien.ac.at/cookbooks/geospatial_data
```
Move into the geospatial_data directory
```
cd geospatial_data
```
Create a virtual environment with all the required libraries and dependencies For that a couple of Options are available:
- uv
- conda
- Docker

uv¶

Create and activate your virtual environment

uv sync --all-extras

Start up Jupyterlab Server in the notebooks directory

uv run jupyter lab notebooks/

Conda¶

Create and activate your conda environment from the environment.yml file

conda env create -f environment.yml
conda activate tucookbooks

Move into the notebooks directory and start up Jupyterlab

jupyter lab notebooks/

Docker¶

When available simply run docker compose to start a Jupyter Lab instance.

Start Jupyter Lab from a Docker container:
```
docker compose up
```
Copy Jupyter Lab URL to web browser:

Stop and remove the container

Stop Docker container with CTRL + C
Remove container:
```
docker compose down
```