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…
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 sheโs 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.
This week, in collaboration with Geoscience Australia, we released our Sentinel-2 Cloud-Optimized GeoTIFF (COG) dataset on AWS Open Data. Our collection contains all 11.4 million scenes from the Sentinel-2 Public Dataset, except the JPEG2000 (JP2K) files are all converted to COGs. Our dataset is continuously updated to mirror the growth of the public Sentinel-2 data…