WATERLOO, Ontario, Dec. 9, 2019 /CNW/ -- SkyWatch is excited to announce that the company was selected by the Canadian Space Agency (CSA) to complete Phase I of the Artificial Intelligence and Big Data Analytics for Advanced Autonomous Space Systems challenge in July 2019. During this phase of the project, SkyWatch will work closely with the CSA to develop and deliver a system concept that aims to demonstrate the technical feasibility and commercial potential of applying artificial intelligence and big data analytics to the data from multiple space missions collected by the CSA.
The purpose of the Artificial Intelligence and Big Data Analytics for Advanced Autonomous Space Systems challenge is to apply artificial intelligence and big data analytics to bring tangible advancements in the operation and utilization of space assets in support of government operations, public safety, public health and discovery. These methods could enable autonomous prediction of natural or man-made disasters and lead to the transition from reactionary imaging in response to crises to new services in predicting and preventing disasters (including fires, floods, disease outbreak, space weather events, etc.).
"SkyWatch's team of engineers has been working successfully for many years now at combining data from multiple Earth observation missions to help a variety of companies and organizations derive new insights from these datasets," said James Slifierz, CEO of SkyWatch. "We look forward to continuing our close relationship with the Canadian Space Agency on this important new project that would enable the agency to better utilize their fleet of imaging satellites."
SkyWatch (www.skywatch.com) is on a mission to make Earth-observation data accessible to the world. Hundreds of trillions of pixels of our planet are captured from space every day. Utilizing our past experience in building satellite data aggregation software, our team is developing EarthCache™, a robust platform allowing developers to discover and access the world's remote sensing datasets.