
Azavea is releasing a dataset consisting of 32 unique Sentinel-2 tiles with cloud labels produced by humans.
Azavea is releasing a dataset consisting of 32 unique Sentinel-2 tiles with cloud labels produced by humans.

At the end of June, Casual Space Podcast hosted by Beth Mund, published a conversation she had with Dan Pilone, Co-Founder and CTO. The conversation covered a lot of ground (pun intended!). It was impressive that Dan was the first person she’s had on her show to talk about the data and informatics side of…
We’re launching a library called Loam. Loam allows GDAL to be used in client-side code in a web application. Loam wraps a version of GDAL compiled to WebAssembly and makes the GDAL API and utilities available to Javascript running from within a web browser.

This release presents a major jump in Raster Vision’s power and flexibility. The newly added features allow for finer control of the model training as well as greater flexibility in ingesting data.

STAC is a specification for enabling online search and discovery of geospatial assets. This post walks through getting started with a number of STAC APIs.

We compare the results of two machine learning models that detect clouds in Sentinel-2 satellite imagery and share pointers about models you can try for yourself.

k6.io provides a flexible base for load testing with a convenient scripting language, and the quickstart docs will get you pretty far, but testing with a session of TMS requests had some unique challenges.

Hear from labeling competition participants on why this diverse group of people came together, from across the world, to collaborate on a hallmark training dataset compliant with the STAC standard.

In this post, we’ll be using some advanced TypeScript libraries to help us stay type-safe even in the face of unknown inputs. In order to get the most from this post, I recommend having a basic understanding of TypeScript.

We joined with Radiant Earth at the Cloud Native Geospatial Sprint to run a labeling competition for non-technical folks. This resulted in over 2.3 million square kilometers mapped and lots of lessons learned.
