The SpaceNet Challenge Round 2
Compete to Create Next-Gen Geospatial Computer Vision Algorithms
CosmiQ Works, DigitalGlobe and NVIDIA are challenging the Topcoder Community to develop automated methods for extracting building footprints 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. DigitalGlobe, 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 recently observed by the need to map buildings in Haiti during the response to Hurricane Matthew. 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 building footprints from high-resolution satellite imagery. Moving towards more accurate fully automated extraction of buildings 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 polygonal areas that represent buildings from satellite images. The polygons your algorithm returns will be compared to ground truth data, and the quality of your solution will be judged by the combination of precision and recall.
** To win the Early Incentive your solution must be the first to reach a threshold of 400,000 for the average F-score of all the cities.
Best F-Score Locations:
Las Vegas - $1,000
Paris* - $1,000
Shanghai - $1,000
Khartoum - $1,000
The SpaceNet Challenge Round 2 offers you multiple ways to win big prizes totalling $15,500. In addition to the top 3 performing algorithms winning prizes, this Topcoder challenge also offers several first-to-target bonus payouts. All challenges are TCO eligible.
THE PRIZES and WAYS TO WIN - $15,500 in Prizes
* The winning entry on the Paris data set must score higher than 400,000, which is above a score derived solely from OpenStreetMaps (OSM).
1st Prize - $6,000
2nd Prize - $3,000
3rd Prize - $1,500
Early Incentive** - $1,000
The SpaceNet Challenge Timeline
* Timeline is subject to slight changes
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!
This Topcoder Challenge has now ended! Check out the problem statement below
Put your skills to the test and create next-gen geospatial computer vision algorithms using real satellite imagery and data!
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