The SpaceNet Off-Nadir Building Detection Challenge has launched
The SpaceNet team has launched the SpaceNet Off-Nadir Building Detection Challenge on TopCoder. The Challenge will run through December 21, 2018 and offer’s $50,000 in total prizes. Read more about the dataset and challenge on the The DownlinQ and visit the Challenge page to register and compete.
This challenge focuses on the use of Off-Nadir imagery for building footprint extraction. The dataset includes 27 WorldView 2 Satellite images from 7 degrees to 54 degrees off-nadir all captured within 5 minutes of each other. The dataset covers over 665 square kilometers of downtown Atlanta and ~126,747 buildings footprints labeled from a nadir image. It is now available for download — for instructions, see the SpaceNet Off-Nadir Dataset page
You can learn more also at the Off-Nadir Building Extraction
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. The SpaceNet Partner’s have committed to accelerating machine learning through four open source key pillars: data, challenges, algorithms, and tools.
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.