![]() ![]() “The results from Round 2 show developers are harnessing machine learning and high-resolution training data to achieve outcomes that indicate automation could be used in the near future to support broader geospatial analytics. “SpaceNet’s innovation is having a meaningful impact within the first year, thanks to the collaboration between developers and data scientists to apply open data and machine learning in the geospatial field,” said Tony Frazier, Senior Vice President and General Manager of DigitalGlobe | Radiant. As in the first challenge, the winning algorithms are available to the open source community through the SpaceNet GitHub repository and users of DigitalGlobe’s Geospatial Big Data platform, GBDX. CosmiQ Works’ blog post provides further technical detail on the top three winning solutions. Such algorithms can help keep maps up-to-date at scale and serve end-users when current information is needed to direct resources, such as first responders during natural disasters. The winning algorithms achieved performance with potential for automated mapping tasks. Building on the success of the first competition, SpaceNet Round 2 incorporated several enhancements, including the release of higher resolution imagery from WorldView-3 (30 cm resolution), improved building footprint training data from four new geographically diverse areas and an extended competition length to provide more time for algorithm development. The SpaceNet Challenge Round 2, hosted on TopCoder, was a continuation of the first competition and invited participants to improve automated mapping algorithms for building footprint feature extraction from overhead imagery. A collaboration between DigitalGlobe, CosmiQ Works, and NVIDIA, SpaceNet leverages an online repository of publicly accessible satellite imagery, co-registered data layers for training algorithms, and prize challenges to accelerate innovation in machine learning. (NYSE: DGI), the global leader in Earth imagery and information about our changing planet, today announced the SpaceNet Challenge Round 2 results and plans for the next two SpaceNet Challenges. The project and its name are a play off of ImageNet, a similar database of images created to help catalyze early advancements in computer vision.DigitalGlobe, Inc. ![]() Researchers will be able to create high-impact geospatial applications by applying our DIGITS deep learning tool to the SpaceNet data corpus. Jon Barker, Solutions Architect at NVIDIA. ![]() Innovation of AI algorithms is fueled by large, high-quality, labeled datasets like SpaceNet and flexible, open-source machine learning tools, said Dr. Both the public and private sector have a lot to gain from better post-capture analysis tools to help automate processes previously relegated to crowdsourcing or painstaking individual search. CosmiQ Works is affiliated with In-Q-Tel, the venture capital arm of the CIA, and helps the intelligence community onboard tools from startups focused on space. NVIDIA is going to provide researchers and developers with tools to take advantage of the new images. In addition to DigitalGlobe, NVIDIA and CosmiQ Works are also supporting the rollout of SpaceNet. The curated set will eventually include more than 60 million labeledhigh-resolution images. The consortium of companies that contributed to SpaceNet want to make sure that the imaging data exists to take advantage of advancements in computer vision and machine learning.As of now, DigitalGlobe is offering 200,000 building footprints across the city of Rio de Janeiro, at no cost. As a result, for the first time, its becoming possible to work through massive, complex data sets in hours and minutes instead of years and months. The satellite imagery in the SpaceNet database will be able to serve as training data for new generations of intelligent analytics tools for deconstructing large quantities of imagery and quickly generating insights.Īs our processing capabilities grow in availability, and our algorithms and statistical tools become more efficient, so-called training time for machine learning is decreasing. Just this week, CrowdAI graced the stage of Y Combinator Demo Days with a platform that promises to leverage computer vision and machine learning to automatically annotate and quantify data hidden within satellite photography. Satellite imaging has also been analyzed to help the Navy find Somali pirates, crowdsource the hunt for Malaysia Airlines flight 370 and identify deforestation zones. With an increase in the number of CubeSats, high-resolution satellites and drones of every shape and size, we have accumulatedpetabytes of imagingdata thatcan beprocessed with analytics to solve myriad problems.ĭigitalGlobe, which operates imaging satellites, has built out partnerships with companies like Facebook to target rural villages with internet access using photography as a guide.
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