Improving your Company’s Workflow with Radiant Earth Foundation

By Pavel Bordioug, Director of Marketing and Business Development, H2O Geomatics

The team behind H2O Geomatics, a University of Waterloo (Canada) research spin-off specializing in satellite and drone-based remote sensing.

H2O Geomatics is an Earth observation company that has been around since 2014. We have worked on a variety of remote sensing contracts for both the European and Canadian Space Agencies and have also expanded into the commercial sector. A large part of our portfolio includes creating Earth observation products to support the agriculture, energy, and insurance industries.

In order to fulfill the varied needs of our clients, H2O Geomatics needs to access large amounts of satellite data and then process it efficiently. Radiant Earth Foundation provides the perfect solution to the challenges H2O Geomatics faces. Being able to access and process imagery over the cloud addresses both the issues of image acquisition and processing speed. Previously, data had to be downloaded from a variety of sources and then processed using onsite computers, taking considerable time and storage space. On Radiant Earth Foundation’s platform however, the data is aggregated and so, the same processing can be done far quicker.

The Radiant Earth Foundation platform also allows for the quick addition of new algorithms through logic trees. Although this feature can’t currently be used for adding our more complex machine learning algorithms, it was perfect for adding a variety of other algorithms, such as our algae detection, permafrost detection, and soil moisture algorithms.

Using just this portion of the platform through the Lab allowed us to speed up our workflow significantly.

We explored Radiant Earth’s API after having already integrated the platform into our workflow and found that the platform had even more potential than we initially expected. The API allows us to customize the system in ways that add a lot of functionality to the system, such as creating time composites (for removing cloud cover) and custom overlays of relevant algorithms, specific to our applications. We are now integrating it with our more sophisticated machine learning algorithms and hope to fully automate many more workflows in the future.

Editor’s note: For a detailed overview of how to use Radiant Earth’s API, watch the webinar recording via our YouTube Channel. The recording includes a case study that showing how to import drone imagery over an area of interest.