Descartes unveils geospatial machine-learning platform

SAN ANTONIO, Texas — Descartes Labs presented a new
geospatial machine-learning platform to potential defense and
intelligence customers June 4 at the U.S. Geospatial
Intelligence Foundation’s 2017 GEOINT Symposium here.

The Decartes Labs Platform pulls in remote-sensing data
from a variety of sources, which customers can search by
location or time to identify objects and forecast change. The
cloud-based platform applies Descartes Labs’ machine-learning
and forecasting models to petabytes of imagery drawn from
multiple satellites including the NASA-U.S. Geological Survey’s
Landsat 8, the Moderate Resolution Imaging Spectroradiometer
(MODIS) instruments flying onboard NASA’s Terra and Aqua, and
the European Space Agency’s Sentinel-1, Sentinel-2 and
Sentinel-3.

Descartes Labs, a spin-off from the U.S. Energy
Department’s Los Alamos National Laboratory, was established in
2014 to apply machine learning to Earth imagery and other large
datasets. Before machine learning can extract value from
imagery drawn from different space-based instruments, however,
the data has to be pre-processed to line up pixels and correct
for varying atmospheric conditions and spectral
calibrations.

At Descartes Labs, we are developing a
science-ready data archive (nearly 10 petabytes and counting)
and cloud compute platform that allow remote-sensing teams to
focus on the machine learning algorithms to solve the
application at hand rather than worry about the data
aggregation, pre-processing, and compute
infrastructure,”
Fritz Schlereth, Descartes Labs’
head of product, told
SpaceNews
by email.

At GEOINT, Descartes Labs also unveiled a new GeoVisual
Search tool that allows users to click on an object and find
objects around the world or in certain geographic areas that
are visually similar. In the accompanying image, for example,
the user clicked on ships in a marina and GVS processed
satellite and aerial imagery in the cloud to identify similar
objects around the world. Users can also use geographic or
temporal parameters to confine the GVS search.

 

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