The SpaceNet Challenge Round 3
Compete to Create Next-Gen Geospatial Computer Vision Algorithms
CosmiQ Works, Radiant Solutions, and NVIDIA are challenging the Topcoder Community to develop automated methods for extracting road networks from high-resolution satellite imagery. Such automated methods will help create more accurate maps, more rapidly.
The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. Radiant Solutions, CosmiQ Works, and NVIDIA have partnered to release the SpaceNet data set to the public to enable developers and data scientists.
Today, map features such as roads, building footprints, and points of interest are primarily created through manual techniques. We believe that advancing automated feature extraction techniques will serve important downstream uses of map data including humanitarian and disaster response, as observed by the need to map road networks during the response to recent flooding in Bangladesh and Hurricane Maria in Puerto Rico. Furthermore, we think that solving this challenge is an important stepping stone to unleashing the power of advanced computer vision algorithms applied to a variety of remote sensing data applications in both the public and private sector.
WHY THIS CHALLENGE MATTERS
Can you help us automate mapping? In this challenge, competitors are tasked with finding automated methods for extracting map-ready road networks from high-resolution satellite imagery. Moving towards more accurate fully automated extraction of road networks will help bring innovation to computer vision methodologies applied to high-resolution satellite imagery, and ultimately help create better maps where they are needed most.
Your task will be to extract navigable road networks that represent roads from satellite images. The linestrings your algorithm returns will be compared to ground truth data, and the quality of your solution will be judged by the Average Path Length Similarity (APLS) metric.
THE PRIZES and WAYS TO WIN - $50,000 in Prizes
The SpaceNet Challenge Timeline
* Timeline is subject to slight changes
The SpaceNet Challenge Asset Library
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Put your skills to the test and create next-gen geospatial computer vision algorithms using real satellite imagery and data!
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This challenge is NOW LIVE !!!