# CNG Conference JupyterGIS abstract Blog post with details: https://cloudnativegeo.org/blog/2026/04/share-your-work-at-cng-forum-2026/ ## Track choice ### Cloud-Native In-Practice From submission page: > This track showcases practitioners actively deploying cloud-native geospatial solutions, tackling real challenges, and delivering measurable results. **These are the stories from the field** — what worked, what didn't, and what you can learn from those who've been there. From blog: >This track is for practitioners currently deploying solutions and solving problems at scale. We want your real-world implementation stories: the technical details, what has worked and didn’t for you, and your lessons from running systems in production. Most importantly, we want to hear about the impact of your work; how you’ve made data easier for your users to access and manage. > > What we’re looking for: As much as possible, we want to hear about use cases rather than general overviews. While we want to hear about the tools you’ve built, your talk should center on the “why” and the “who.” How is your tool changing your users’ workflows? What questions have been answered more effectively because of your tool? > > To keep the focus on “stories from the field,” we’ll prioritize submissions that are user-focused. If you’re a tool builder, we encourage you to focus on how your users solved a specific problem, or co-present with them. > > We also welcome more technical submissions that go deep on architecture, implementation, or engineering challenges, even if they aren’t centered on a specific use case. Note that some technical talks may be better suited for a breakout session lightning talk or a Birds of a Feather discussion rather than a traditional talk slot. Depending on submissions, we may reach out to suggest that format during the submission review process. When submitting, please indicate in the form whether your talk is use case-focused or more of a technical deep dive so we can plan accordingly. Good for a demonstration of using JupyterGIS and OpenEO or jupyter-xarray-tiler on a CNES concrete use case. Target: evolution of snow cover over a given area, computed on the fly. ### On-Ramp to Cloud-Native Geospatial From submission page: > This track is a welcoming space for those beginning their journey into cloud-native geospatial technologies. Whether you're making the **transition from traditional desktop GIS**, teaching others, or **building accessible tools and resources**, this track focuses on **practical pathways** and proven approaches to get started. From blog: >This track is a welcoming space for anyone transitioning to web-based workflows or just starting to work with cloud-optimized geospatial formats. We want to hear from you if you are **helping others ditch the “download-and-wait” workflow**, teaching people how to use these modern formats, or **building tools that make the web feel as practical as a desktop application**. This track focuses on the practical steps and the know-how to stop managing local files and start working directly on the web. > > What we’re looking for: We want to see hands-on introductions to core formats like COGs, GeoParquet, STAC, Zarr, and vector tiles. We’re looking for submissions that share training materials, real-world lessons from those who’ve made the switch, and honest looks at the common pitfalls to avoid. Good for an overview or tutorial in using different features of JupyterGIS? Through Jupyterlite/notebook.link? Focus on some of the new stuff? Maybe story telling? New symbology UX? What would be most impactful? ### Small Technical Workshop Tutorial + implementing use case or training material using JupyterGIS new features? Q: Does a workshop enable us to show something new and useful? Is there JGIS development that's needed for a successful workshop? Does QS have time to support that dev? Q: Do we have the capacity to prepare a workshop? Matt and Guillaume are both leaning towards "no". This might need to be a collab? ## Abstract proposal on GeoJupyter news (Matt) ### Authors Matt Fisher, Fernando Perez, Maya Weltman-Fahs, Kirstie Whitaker, Maryam Hosseini, Carl Boettiger, Guillaume Eynard Bontemps, Martin Renou, Vincent Sarago, Arjun Verma, Greg Mooney, Nakul Verma, Matthias Meschede, Nicolas Brichet, Meriem BenIsmail, Sylvain Corlay, Florence Haudin, David Brochart, Trung Le Duc ### Title GeoJupyter, a community to reimagine geospatial interactive computing in Jupyter: What is happening and what is next? ### Abstract GeoJupyter is an open and community-owned effort to reimagine geospatial interactive computing and visualization experiences within the extended Jupyter ecosystem. We aim to combine the approachability and playfulness of desktop GIS tools, the flexibility and reproducibility of coding-driven GIS methods, and the collaborative and storytelling power of Jupyter to enable researchers, educators, and learners to more confidently engage with geospatial data. In this talk, we'll give an overview of recent advances in the GeoJupyter community. We'll cover new JupyterGIS features, including a GUI-driven story-builder interface, a new symbology system, improved STAC support, and more; `jupyter-xarray-tiler`, a tool for managing tile servers in Jupyter; and our new 2i2c-inspired practices for dynamic, open, and community-driven roadmapping. We'll also discuss our vision for possible future projects, including document-driven storytelling, improving reproducibility-by-default in GUI-driven geospatial workflows, better geospatial primitives in Python, and exploring practical frameworks for using LLMs in geospatial workflows, including a prototype, `jupyter-geoagent`. Finally, we'll invite the audience to help set a course towards a less frustrating and more joyful geospatial future, and show concrete ways they can get involved. ## Abstract proposal on CNES use case (Guillaume) ### Authors Guillaume Eynard Bontemps, Martin Renou, Vincent Sarago, Matt Fisher, Arjun Verma, Greg Mooney, Nakul Verma, Matthias Meschede, Nicolas Brichet, Meriem BenIsmail, Sylvain Corlay, Florence Haudin, David Brochart, Trung Le Duc ### Title Using JupyterGIS for data analysis and visualization at scale in the context of Snow Detection ### Abstract Cloud-native technologies and standards are creating new possibilities for data analysis at scale. Until recently, Google Earth Engine (GEE) was the only practical solution for scientists wanting to test algorithms at scale and visualize results quickly. With the advancement of the geospatial and Python ecosystems supported by the Pangeo, CNG, and OGC communities, we've seen open-source tools and standards -- including Jupyter, Xarray, Dask, cloud-optimized formats, STAC, and OpenEO -- closing the gap with GEE. Still, there is a shortage of easy-to-use interactive visualization tools for working with modern datacubes and data streams enabled by these new developments. In this talk, we will present specific work from the GeoJupyter community, based on JupyterGIS for the frontend and TiTiler for the backend, to enable interactive computation at scale. We'll demonstrate a [CNES (French space agency)](https://cnes.fr/en) use case for snow detection over the Pyrenees mountains. It shows how these technologies can interactively display the results of a computation performed on-the-fly on geospatial data streamed from cloud-optimized and open Sentinel-2 data. We'll compare alternative methods, including: - Using OpenEO as the data source, which enables reproducible visualization by serializing a processing graph inside a GIS document. - Using full Xarray syntax with `jupyter-xarray-tiler`, powered by TiTiler, which enables visualizing a datacube with custom computations. ## TODO - [x] Matt: Figure out what length the talks are supposed to be (email Jed/Amy/Michelle) - Email + text sent, waiting response - Resolved: 20 minutes per talk, including questions - [x] Matt: Figure out if we want to have two talks or just one joint talk - Guillaume: 20 minutes including questions, means 15 minutes presentation, I think we've got enough materials for two talks? - Resolved: For now, let's plan on two talks and consolidate before submitting on Friday if we need to. - [x] Depending on which we decide, figure out how to split the subject matter between Matt & Guillaume - Matt: Re-introduce GeoJupyter, what's new, demo(s), what's next - Symbology - Document based story building - Guillaume: Overview & demo of CNES-funded work. Going beyond the bounds of what we could do before in open source, and wider than JupyterGIS. - [x] Decide on a track! * Decided! On-ramp. - [x] Polish abstract(s) - [x] Submit - [ ] Implement rad stuff in JupyterGIS - [ ] Make the talks in August/September - [ ] Practice - [ ] Think about naming/messaging/language we use: "Open Planetary Engine"? Open alternative anyone can deploy on their own infrastructure, not necessarily bringing all the baggage of Planetary Computer or Earth Engine. Empower you the user to leave GEE or Planetary Computer and do the same things openly. Build an ecosystem that can compete. ## Misc notes * Matt thinking a lot about the future of storytelling * https://explorer.eopf.copernicus.eu/story/?id=ndci * Matt: Want to build **document-driven** story telling. Starting with a specification (e.g. Closeread Spec), then reference implementation in MyST.