BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//talks.staging.osgeo.org//qgis-uc2025//speaker//WQVRHR
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-qgis-uc2025-WKM9JT@talks.staging.osgeo.org
DTSTART;TZID=CET:20250602T160000
DTEND;TZID=CET:20250602T163000
DESCRIPTION:Desktop GIS can be a bit of a headache for users\; especially n
 ewcomers. You've probably been there - downloading large geospatial datase
 ts to local machines that take forever\, wrestling with complicated softwa
 re installations\, and often being constrained by local compute and networ
 king resources when you push it too hard. It's especially frustrating in w
 orkshops\, where technical hiccups can eat up precious teaching time and e
 veryone's different setup can cause all sorts of problems.\n\nThat's why w
 e're excited about a new approach: running QGIS in the cloud through [Jupy
 terHub](https://github.com/jupyterhub/jupyterhub). This talk presents our 
 prototype implementation of running QGIS in a JupyterHub environment\, a c
 ollaboration between researchers from [QGreenland](https://qgreenland.org/
 )\, [2i2c](https://2i2c.org/)\, [Development Seed](https://developmentseed
 .org/)\, and NASA exploring how this integration could potentially reduce 
 technical barriers.\n\nWe'll demonstrate how JupyterHub can serve a QGIS d
 esktop environment through a web browser\, potentially simplifying the ins
 tallation process and reducing local hardware requirements. The allows use
 rs 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 infras
 tructure can be dynamically scaled to match computational demands\, allowi
 ng users to adjust RAM and CPU resources based on their specific processin
 g needs. Having QGIS and Jupyter notebooks running on the same machine ena
 bles fluid workflows where users can seamlessly switch between visual GIS 
 analysis and programmatic data processing without data transfer overhead.\
 n\nWe'll also discuss our work with [jupyter-remote-qgis-proxy](https://gi
 thub.com/sunu/jupyter-remote-qgis-proxy)\, which builds QGIS-specific feat
 ures on top of [jupyter-remote-desktop-proxy](https://github.com/jupyterhu
 b/jupyter-remote-desktop-proxy). We're exploring capabilities like shareab
 le links that load specific datasets and layers in QGIS\, streamlining dat
 aset access for collaborators.\n\nFinally\, we'll talk about some of the c
 urrent limitations of this approach of running QGIS in the cloud and look 
 at promising projects like [JupyterGIS](https://github.com/geojupyter/jupy
 tergis/) that could help create an even better\, more collaborative web-ba
 sed GIS experience.
DTSTAMP:20260527T090547Z
LOCATION:Statisten
SUMMARY:QGIS meets JupyterHub: Taking Desktop GIS to the Cloud - Tarashish 
 Mishra
URL:https://talks.staging.osgeo.org/qgis-uc2025/talk/WKM9JT/
END:VEVENT
END:VCALENDAR