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The SpaceNet  Challenge Round 4 


Compete to Create Next-Gen Geospatial Computer Vision Algorithms

CosmiQ Works, Digital Globe and Radiant Solutions 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. CosmiQ Works, Digital Globe and Radiant Solutions 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

CHALLENGE OBJECTIVE

Can you help us automate mapping from off-nadir imagery? In this challenge, competitors are tasked with finding automated methods for extracting map-ready building footprints from high-resolution satellite imagery from high off-nadir imagery. In many disaster scenarios the first post-event imagery is from a more off-nadir image than is used in standard mapping use cases. The ability to use higher off-nadir imagery will allow for more flexibility in acquiring and using satellite imagery after a disaster. Moving towards more accurate fully automated extraction of building footprints 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 building footprints from increasingly off-nadir satellite images. The polygon’s you create will be compared to ground truth, and the quality of the solutions will be measured using the SpaceNet metric


THE PRIZES and WAYS TO WIN - $50,000 in Prizes

The SpaceNet Challenge Timeline

Winners Announced!

01/04/19

Match Ends!

12/21/18

* Timeline is subject to slight changes

Match Begins!

10/19/18

The SpaceNet   Challenge Asset Library

Want more resources for The SpaceNet Challenge? Check out the asset library, full of articles and information that can help you compete successfully in this challenge!

New to Topcoder? Join the Topcoder Community here so you can participate in this amazing challenge and many more like it!

Put your skills to the test and create next-gen geospatial computer vision algorithms using real satellite imagery and data!

This challenge is live!