Descartes Labs launched as a spin-out from Los Alamos National Laboratory in April of 2015. The underlying technology uses computer vision, machine learning, and cloud-based infrastructure to teach computers to see and understand the world around them. Initially, the technology was applied to develop an agricultural model to analyze corn and soy production in the United States. Using imagery from various satellite constellations, weather data, and other datasets, the model accurately predicted yield six months prior to the end of the growing season. Realizing the potential for the technology applied to use cases around the globe, Descartes Labs turned its focus to developing a cloud-based supercomputing platform for the application of machine intelligence to massive data sets. This platform could be used by anyone to develop intelligent learning models to do science on data, and make that data actionable. Capitalizing on the confluence of advances in AI and high-performance cloud computing – along with the rapid increase of sensors capturing information all over the globe – Descartes Labs has created an enterprise data refinery that enables organizations to model – and predict changes in – natural resources and the entire value chains in which they live. With an initial focus on satellite imagery, Descartes Labs processes a pipeline of data flows from all the major satellite constellations, at scale, to provide instant access to analysis-ready images of the entire world via a massive, searchable, on-demand interface. For example, petabytes of imagery can be searched and retrieved, and all features from various constellations accessed, in under 100 milliseconds. By collecting, normalizing, and presenting science-ready data generated by satellites, the Descartes Labs Platform is advancing and accelerating unique applications of machine intelligence for consumers, corporations, and governments worldwide. Descartes Labs is headquartered in Santa Fe, New Mexico, with offices in San Francisco, Los Alamos, and New York City.
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