
We outline our 10% time program in detail, highlight some notable examples and themes, and describe the impact of the program.
We outline our 10% time program in detail, highlight some notable examples and themes, and describe the impact of the program.

In this blog we demonstrate how an active learning approach can boost machine learning model performance with the human-in-the-loop workflow.

In order to benchmark efficiency, we take a deep dive into Zarr and Parquet data retrieval to compare performance on various time scales.

Due to Next.js’ ability to populate webpages in remote areas, we used it to build a decision-support tool that conveys landslide risk.

We donate a portion of our profits each year to support open source projects nominated by our team.

What is the Climate Change Learning Group? At Azavea, we’ve woven climate initiatives into the identity of our business. As one of the greatest threats to our planet’s future, climate change is something that unites and motivates our team. Recent hires at Azavea overwhelmingly cite climate work as a key motivator for joining the organization.…

The US Census counts most people in their residence using the household survey, but the group quarters count is particularly relevant to drawing district boundaries and impacts the redistricting process.

About a year ago, I started on a new project at Element 84 working with geospatial imagery. In this blog post, I will document some things I learned on my journey. My hope is these lessons learned will help encourage more generalist software engineers to start experimenting with geospatial data and tooling. I had never…
20 years on, Azavea has never taken on investor capital. Our CEO, Robert Cheetham, explains why this is the case.

A recent project required us to implement an interactive map of the United States with a custom counties layer. This is what we learned.
