YouTube video by AI for Good

A CNG community gathering in New York City during NYC Climate Week.

We discuss the evolution of Queryable Earth into Agentic Queryable Earth, including a more conversational experience.

We revisit the question: is Zarr the new COG? We review changes from the past year and discuss the shrinking gap between the two data formats.

In this blog we give a sneak preview into the typical conversation structure we have with new-to-us organizations through frequently asked questions that we regularly answer during introductory calls.

In this blog, we sit down with Mark Reese to discuss his personal connection to his work, and how he sees E84’s work in the federal space continuing to evolve.

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For businesses operating in highly regulated industries, zero risk tolerance policies often lead to delays in AI adoption. Although there are certainly risks

Sharing our findings from the past year helping NASA build and scale a chat-based AI tool called ChatGSFC.

We present highlights from our ongoing open-source work aimed at building conversational interfaces to query and analyze NOAA data using LLMs.

We detail how we caption Queryable Earth tiles. We also share the tools we use for validating our results and comparing the generated embeddings.

Reflecting on the Cirrus story, three core lessons emerged that seem worth discussion, lessons that apply to software projects generally.

Earlier this week, I participated in an amazing sprint to define common standards, best practices, and documentation for geospatial vector embeddings. I have some initial takeaways to share.

In order for AI to task satellites and act in the physical world at scale, the community must to settle on an agreed upon standard.

A retrospective look at the history of Cirrus, a data orchestration framework we develop and use at E84, and where we plan to go from here.

The geospatial domain offers unique perspective into the recent shift in AI coding and related impacts on the entire open-source ecosystem.

We detail how we used queryable Earth and natural language geocoding to demonstrate data accessibility in an intuitive and interactive way.

Why Machine-Writing Code is the Best (and Most Dangerous) Thing for Geospatial:   The current discourse surrounding AI coding is nothing if not polarized. On

The geospatial domain offers unique perspective into the recent shift in AI coding and related impacts on the entire open-source ecosystem.

We collaboratively designed and developed BoundarySync, a free and open source tool that streamlines water boundary maintenance.

We share our response to a data outage incident and how our response ultimately increased available data and decreased latency for our client.