DIGITALGLOBE ANNOUNCES SPACENET CHALLENGE RESULTS AND PLANS FOR FOLLOW-ON COMPETITION
WESTMINSTER, Colo. - DigitalGlobe has announced the results of the first SpaceNet Challenge and the next phase of the initiative. SpaceNet is a collaboration between DigitalGlobe, CosmiQ Works, and NVIDIA, which consists of an online repository of freely available satellite imagery, co-registered map layers to train algorithms, and public challenges that help accelerate development of machine learning.
The first SpaceNet Challenge was launched in November 2016, and 42 developers competed in an open challenge hosted by TopCoder to create algorithms that extract building footprints from satellite imagery. The participants submitted 242 solutions over a three-week period to compete for a total prize pool of $35,000 that was awarded to the top five performing contestants. The winning algorithms will be made available to the open-source community through the SpaceNet GitHub repository and users of DigitalGlobe’s Geospatial Big Data platform, GBDX.
The next phase of the SpaceNet Challenge will be a follow-on competition utilizing DigitalGlobe’s highest-resolution 30 cm imagery from WorldView-3 and building footprints across new locations around the globe. Developers will be challenged to improve performance from the first competition using the higher-resolution imagery and more geographically diverse training data samples. Further details of this challenge will be provided in the weeks ahead.
These challenges are a reflection of some of the latest trends within the remote sensing satellite market. As imagery data becomes more widely available, the focus of the industry is shifting to processing and analyzing data to create actionable intelligence.
The challenges also demonstrate the increasing influence of Silicon Valley on the remote sensing industry. The industry once was a closer reflection of the U.S. military, which was its most important customer. This necessitated more secretive operations. However, as commercial applications for remote sensing data become more widespread, the industry has increasingly turned to open source business models common in Silicon Valley.
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