The task of SpaceNet 6 was to automatically extract building footprints with computer vision and AI algorithms using a combination of synthetic aperture radar (SAR) and electro-optical (EO) imagery datasets. SpaceNet 6 added further complexity with multi-sensor all weather mapping and upped the difficulty level. The SpaceNet 5 challenge sought to build upon the advances from SpaceNet 3 and tested challenge participants to automatically extract road networks and routing information from satellite imagery, along with travel time estimates along all roadways, thereby permitting true optimal routing. SpaceNet 4 focused on automating mapping from off-nadir imagery. SpaceNet 3 challenged participants to detect and map road networks across four cities. SpaceNet 2 had a similar focus, but drew on much more data and multiple new imagery formats, among other advancements. In SpaceNet 1, the Topcoder community developed automated methods for extracting building footprints from Maxar’s high-resolution satellite imagery. The SpaceNet challenge series is unique in that it’s incremental-each challenge builds upon the last with an additional layer of complexity. History Snapshot: Topcoder and the Spacenet Challenge Series The algorithms also help SpaceNet make breakthroughs in advanced geospatial vision algorithms. Over the six challenges that SpaceNet has run on Topcoder, they gained algorithms relating to geospatial mapping that could prove useful in disaster relief and non-commercialized mapping. That’s the beauty of using open talent for data science. With many people developing unique algorithms, SpaceNet can compare them against each other to find the best fit. Through crowdsourcing and on-demand talent, SpaceNet can open up the problem to hundreds of thousands of minds around the globe. Collaborating with Topcoder’s crowd makes producing all of these algorithms possible, which might not be possible with SpaceNet’s relatively small dedicated team. SpaceNet is working hard to leverage machine learning algorithms that can fully automate the extraction of building footprints and road networks from satellite imagery. The Power of Many Minds on a Single Problem – Spacenet and Open Talent In the video below leaders from several of the key SpaceNet partners discuss the history of SpaceNet, it’s shared purpose, and where the program is heading next. In this Uprisor Innovation conversation, hear from the SpaceNet team about the Topcoder Challenge series, the results of SpaceNet 6, and why they believe in the powerful combination of open data initiatives + open talent (aka crowdsourcing).īeyond open data and open talent, SpaceNet is an incredible collaboration of partners including Maxar Technologies, Capella Space, AWS, and others. Each SpaceNet challenge focuses on a different aspect of applying machine learning to solve difficult mapping challenges. Since 2016, SpaceNet has leveraged the Topcoder community to solve an increasingly complex series of data science problems. SpaceNet aims to create accurate machine learning algorithms that can identify building blueprints and road networks from images. building footprint & road network detection). The open innovation project SpaceNet, a nonprofit partnership led by co-founder and managing partner, CosmicQ Works and co-founder and co-chair, Maxar Technologies, is dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e.
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