US: The U.S., New Mexico-based startup, Descartes Labs has announced to give the access of its Cloud-based parallel computing infrastructure system to a handful of developers. The company built its own Cloud-based parallel computing system some time ago to clean and process its massive corpus of satellite imagery.
Since throwing raw satellite imagery into machine learning models wouldn’t be great from the point of extracting insights, the Descartes Labs’ Cloud computing system can be useful for capturing images of clouds, cloud shadows and other atmospheric aberrations that make it impossible to compare images taken at different times.
A small cloud over a field, for example, that wasn’t present in previous images, could completely throw off a model attempting to predict crop yields.
To overcome this challenge, engineers can use composite images to optimize for the best pixels across a collection of images. Google Maps employs composite imagery to remove clouds and create representations of the globe that are evenly lit by the sun.
The problem with combining dozens of satellite captures of the entire earth is that it’s incredibly computationally intensive. This is where Descartes Labs’s processing engine comes into play to convert into composites the petabytes of geospatial data it has.
Descartes is limiting the group with access to the underlying infrastructure for the time being. But anyone can spend time this afternoon examining the Sentinel and LandSat composites the team created.