October 12, 2020 7 (and counting) Data Science Geospatial challenges – The SpaceNet Story

The range of skills in Topcoder’s global crowd is enormous. Most people don’t realize that you can do complex heavy work through crowdsourcing. In data science, one of the ways we do this is thorough purposeful series building. Each challenge in a series builds on the advances of the last, getting incrementally more complex and resulting in more useful algorithms over time. Crowdsourcing is ideal for this because competitors can draw on past learnings and solutions to create the next best algorithm, and iterate on an exponential track.

The spacenet story

The SpaceNet challenge series is a fantastic example of this kind of incremental data science series. Each SpaceNet challenge focuses on a different aspect of applying machine learning to solve difficult geospatial computer vision problems. Take a look at this infographic to discover how each challenge has gotten more complex.

Shining a light on our all-star community

Many competitors from the Topcoder community have participated in multiple SpaceNet challenges over the years, and the graphic shows which competitors rank at the top of SpaceNet’s all-star leaderboard.

Thanks to our partners at In-Q-Tel, CosmiQ Works, Maxar Technologies, Intel AI, Amazon Web Services (AWS), Capella Space, and Planet.

If you enjoy this, check out our Topcoder documentary – The Passion Economy: How the Future Works.

Annika Nagy


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