Desktop GIS can be a bit of a headache for users; especially newcomers. You've probably been there - downloading large geospatial datasets to local machines that take forever, wrestling with complicated software installations, and often being constrained by local compute and networking resources when you push it too hard. It's especially frustrating in workshops, where technical hiccups can eat up precious teaching time and everyone's different setup can cause all sorts of problems.

That's why we're excited about a new approach: running QGIS in the cloud through JupyterHub. This talk presents our prototype implementation of running QGIS in a JupyterHub environment, a collaboration between researchers from QGreenland, 2i2c, Development Seed, and NASA exploring how this integration could potentially reduce technical barriers.

We'll demonstrate how JupyterHub can serve a QGIS desktop environment through a web browser, potentially simplifying the installation process and reducing local hardware requirements. The allows users to access and analyze geospatial datasets through a familiar interface, with the key advantage that compute resources reside close to the data, eliminating the need to download large datasets locally. The cloud infrastructure can be dynamically scaled to match computational demands, allowing users to adjust RAM and CPU resources based on their specific processing needs. Having QGIS and Jupyter notebooks running on the same machine enables fluid workflows where users can seamlessly switch between visual GIS analysis and programmatic data processing without data transfer overhead.

We'll also discuss our work with jupyter-remote-qgis-proxy, which builds QGIS-specific features on top of jupyter-remote-desktop-proxy. We're exploring capabilities like shareable links that load specific datasets and layers in QGIS, streamlining dataset access for collaborators.

Finally, we'll talk about some of the current limitations of this approach of running QGIS in the cloud and look at promising projects like JupyterGIS that could help create an even better, more collaborative web-based GIS experience.