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Spacenet logo4/24/2023 Below are the summaries of each component. How Did the Algorithms Measure Up?Īs a way to measure success, SpaceNet assessed the results against five key components. The results were measured using the SpaceNet metric. The created polygons in the top five algorithms were compared with the actual buildings. The competitors were to develop algorithms that generate polygons correctly outlining the boundary of each identifiable building. The dataset covered 665 square kilometers of downtown Atlanta with 27 worldview images from 7 to 54 degrees off-nadir, with approximately 126,000 buildings labeled with a footprint. It would help create better maps in urgent situations. The ability to work with these off-nadir images and accurately extract building footprints is vital. Images acquired after a disaster are frequently more off-nadir than standard mapping images, as the satellite is not always directly above the disaster area. The competing algorithms attempted to extract map-ready building footprints from high off-nadir imagery. In other words, ‘off-nadir’ is satellite imagery taken at an angle, not directly above, the location. The challenge focused on off-nadir imagery for building footprint extraction. To discover if off-nadir imagery can help automate mapping, SpaceNet launched an off-nadir building detection challenge by crowdsourcing with Topcoder. SpaceNet is on a mission to accelerate geospatial machine learning. The Challenge Defined Crowdsourcing the Algorithm Read on for a short value-prop summary of the Topcoder SpaceNet Challenge. It used Topcoder’s expertise to crowdsource computer vision algorithms in a push to advance mapping automation. One such success story is SpaceNet’s Off-Nadir Building Footprint Extraction Challenge.
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