It’s worth explaining that the SpaceNet team creates hand-labeled datasets since they are intended to serve as AI/ML training datasets. Given Maxar’s role as part of the team to produce the SpaceNet 5 dataset, we thought this would be a good opportunity to share a glimpse “behind the scenes.” Generating labeled training datasets is one of the toughest aspects of SpaceNet. That said, limitations to using algorithms for fully automated map production remain, such as improving the quality and generalizability of outputs for any area in the world.
Over the last three years, SpaceNet has shown how AI/ML computer vision algorithms applied to overhead imagery can automate tasks such as building footprint and road network extraction. Keeping foundational maps up to date, including roads, continues to remain a labor- and cost-intensive aspect of generating map data. Routing is an important use case since it is essential for many humanitarian, civil, military and commercial applications. We are especially excited about the release of the SpaceNet 5 dataset announced August 22, which revisits the challenge of automated road network detection and routing, while adding the complexity of estimating travel time based on distance and road type. SpaceNet is a collaborative initiative between Maxar Technologies, CosmiQ Works, Intel ® AI, Amazon Web Services, and Capella Space.
Maxar is excited to continue our support of SpaceNet, a non-profit organization we helped launch in August 2016 that’s dedicated to accelerating open source, artificial intelligence (AI), and machine learning (ML) applied research for geospatial applications.
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