![]() The first 20 competitors to reach a score of 50 (out of a possible 100) received a credit for 10 hours on a p3.2xlarge for training and improving their models. For the first time in SpaceNet history, the final submissions were tested on a mystery city dataset that was revealed and open sourced at the end of the Challenge. SpaceNet open sourced new data sets for the following cities: Moscow, Russia Mumbai, India and San Juan, Puerto Rico. You can find a detailed description of CosmiQ Works’ algorithmic baseline on their blog at The DownLinQ. The task of this challenge was to output a detailed graph structure with edges corresponding to roadways and nodes corresponding to intersections and end points, with estimates for route travel times on all detected edges. The SpaceNet 5 challenge sought to build upon the advances from SpaceNet 3 and test 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. ![]() Satellite or aerial imagery often provides the first large-scale data in such scenarios, rendering such imagery attractive. This statement is as true today as it was two years ago when the SpaceNet Partners announced the SpaceNet Challenge 3 focused on road network detection and routing. In a disaster response scenario, for example, pre-existing foundational maps are often rendered useless due to debris, flooding, or other obstructions. Determining optimal routing paths in near real-time is at the heart of many humanitarian, civil, military, and commercial challenges.
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