Adeel Hassan
Employers on the program
- 2026Element 841 talk
- 2022Azavea1 talk
The year around now
Talks in the past 12 months and announced talks ahead, one row per conference, January to December. Hover a mark for the talk.
Appearances by year
- FOSS4G North America
- CNG Forum (Cloud-Native Geospatial Conference)
- FOSS4G
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| Oct 6, 2026 | CNG Forum 2026 | CNG Forum (Cloud-Native Geospatial Conference) | speaker | Untitled as Element 84 | |
| Nov 3, 2025 | FOSS4G North America 2025 | FOSS4G North America | speaker | Bringing it all together: Zarr, Dask, Knowledge Graphs, and LLMsWe describe our open source work that makes NOAA climate datasets discoverable and queryable via a chat interface, building on top of the incredible open source…Show lessWe describe our open source work that makes NOAA climate datasets discoverable and queryable via a chat interface, building on top of the incredible open source cloud-native geospatial ecosystem to bring science data closer to users than ever before. | |
| Sep 9, 2024 | FOSS4G North America 2024 | FOSS4G North America | speaker | Embed all the things: the promise of geospatial vector embeddingsGeospatial foundation models have arrived and are getting better. We examine how the vector embeddings they provide can be used for clustering, change…Show lessGeospatial foundation models have arrived and are getting better. We examine how the vector embeddings they provide can be used for clustering, change detection, and natural language-search, and discuss the challenge of scaling these solutions across time and space. | |
| Aug 22, 2022 | FOSS4G 2022 | FOSS4G | speaker | Human-in-the-loop Machine Learning with Realtime Model Predictions using GroundWork and Raster Vision as Azavea Acquiring and labeling geospatial data for training machine learning models is a time-consuming and expensive process. It is made even more difficult by the…Show lessAcquiring and labeling geospatial data for training machine learning models is a time-consuming and expensive process. It is made even more difficult by the lack of specialized open-source tools for dealing with the idiosyncrasies of geospatial data. |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Matthew Hanson | 4 | — | 2026 |
| Pete Gadomski | 4 | — | 2026 |
| Jason Gilman | 3 | — | 2026 |
| Andrea Aime | 3 | — | 2025 |
| Corey White | 3 | — | 2025 |
| Giovanni Allegri | 3 | — | 2025 |
| Michele Tobias | 3 | — | 2025 |
| Nathan McEachen | 3 | — | 2025 |
| Stefano Bovio | 3 | — | 2025 |
| Brad Andrick | 2 | — | 2026 |
| Calvin Metcalf | 2 | — | 2026 |
| Colton Loftus | 2 | — | 2026 |
| Daniel Dufour | 2 | — | 2026 |
| Jarrett Keifer | 2 | — | 2026 |
| Jennings Anderson | 2 | — | 2026 |
Speakers with the same name are merged into one entry, so this page may combine more than one person. Affiliations are shown as each conference listed them.