Spacenet maryland12/18/2023 PS-RGB: pan-sharpened version of Red-Green-Blue bands from the multispectral product, with dynamic range adjustment (DRA) applied (3 channels, 3*8-bit, ~30 cm resolution). MS: 8-band multi-channel (8*16-bit, ~1.2m resolution). PAN: panchromatic (single channel, 16-bit grayscale, ~30 cm resolution) (Note: these image types were named differently in Spacenet 3) Please watch the contest forum for updates.įour types of images are available for the target areas. You can access the training and testing data, and all contest resources like a visualizer tool, but can't make submissions yet. Important note : Scoring is not enabled at contest launch time. The most important difference is that now you should report estimated travel times for the extracted route network, and scoring will be based on time instead of edge lengths. This problem specification largely overlaps with that of the specification of SpaceNet Challenge 3, however, there are important differences that should be carefully noted by contestants who participated in SpaceNet 3. CosmiQ Works’ blog, The DownLinQ, provides additional information including an APLS metric overview and detailed description of SpaceNet 5 motivation and structure. In building off of the results from SpaceNet 3, this challenge will use a modified version of the Average Path Length Similarity ( APLS ) metric that is tuned to optimize travel times between nodes of interest. For the first time in SpaceNet history, the final submissions will be tested on a mystery city data set that be revealed and open sourced at the end of the Challenge! SpaceNet will be open sourcing new data sets for the following cities: Moscow, Russia Mumbai, India and San Juan, Puerto Rico. Your task will be 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. This challenge seeks to build upon the advances from SpaceNet 3 and by challenging competitors 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. In a disaster response scenario, for example, pre-existing foundational maps are often rendered useless due to debris, flooding, or other obstructions. This statement is as true today as it was two years ago when the SpaceNet Partners announced SpaceNet Challenge 3 focused on road network detection and routing ( Challenge Results ). Determining optimal routing paths in near real-time is at the heart of many humanitarian, civil, military, and commercial challenges.
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