Recently, we announced the winners of the Rio de Janeiro building footprint extraction competition. In the announcement, we promised to open source the winning implementations and release satellite imagery over additional cities. As of today, the source code for the winning implementations can be found in the SpaceNetChallenge GitHub repository. Additionally, satellite imagery for four new cities is currently available via SpaceNet on AWS. Please continue to read for a summary of the implementation approaches and details on the new imagery.

Winning Implementations

The winning implementation was developed by Brazilian TopCoder wleite with a final footprint evaluation metric score…

The goal of SpaceNet is to catalyze the development of new techniques to automate the analysis of imagery from remote sensors, including satellites. To achieve success in this effort, we have adopted a four pillar “open” strategy. The first pillar is to release openly licensed satellite imagery with associated geotagged labels that can be used to develop machine-learning algorithms. SpaceNet’s inaugural open data release was imagery of Rio de Janeiro taken from the WorldView-2 satellite at 50cm GSD using eight spectral bands. The machine learning labels for the original Rio imagery were building footprint polygons (see example below).

Rio’s Maracanã Zone

The second…

Todd Stavish

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