We compare results to past SpaceNet challenges and note that despite the challenges of identifying small buildings in moderate resolution (4m) imagery, the pixels of SpaceNet 7 seem to overachieve when compared to SpaceNets past. In this post we dive into some of the building-level metrics for the SpaceNet 7 Multi-temporal Urban Development Challenge. SpaceNet is run in collaboration by co-founder and managing partner CosmiQ Works, co-founder and co-chair Maxar Technologies, and our partners including Amazon Web Services (AWS), Capella Space, Topcoder, IEEE GRSS, the National Geospatial-Intelligence Agency and Planet. The goal of this challenge will be to create algorithms that automatically detect objects from more diverse angles than current nadir imagery.Preface: SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e., building footprint & road network detection). The fourth competition will focus on processing off-nadir imagery produced by high-revisit constellations, like DigitalGlobe’s next-generation WorldView Legion constellation, in unprecedented rapid timelines and at global scale. This challenge will utilize machine learning to gain insights into transportation networks and navigation. The third SpaceNet Challenge will shift focus from automated building footprint extraction to road network extraction and optimized vehicle routing. We are excited to grow the SpaceNet training datasets library for an even broader range of applications and hosting additional public competitions to further machine learning capabilities in the future.” “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. 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.įor further technical details, you can log on to CosmiQ Works. The winning algorithms achieved performance with potential for automated mapping tasks. The SpaceNet Challenge Round 2 was a continuation of the first competition and invited participants to improve automated mapping algorithms for building footprint feature extraction from overhead imagery.īuilding on the success of the first competition, SpaceNet Round 2 incorporated several enhancements, including the release of higher resolution imagery from WorldView-3, improved building footprint training data from four new geographically diverse areas and an extended competition length to provide more time for algorithm development. A collaboration between DigitalGlobe, CosmiQ Works, and NVIDIA, the SpaceNet challenge 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. US: DigitalGlobe has announced the SpaceNet Challenge Round 2 results and plans for the next two SpaceNet Challenges. The SpaceNet Challenge Round 2 results has been announced for the next two SpaceNet Challenges.

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