Ff11 spacenet cape2/25/2023 ![]() ![]() ![]() The Multi-Temporal Urban Development Challenge will start on August 31st and will last two months. We hope that challenges like this one will serve as catalysts for the development of novel techniques to extract more meaningful information from spatio-temporal datasets. Combined with the strong background variability between images, object matching and tracking becomes trickier than in typical video deep learning models. In contrast with common computer vision tasks, patterns are much more textural and occur at small scales. In addition to the humanitarian benefits and practical applications, the data set presents novel challenges in the fields of remote sensing and machine learning. This will have an impact on disaster preparedness, the environment, infrastructure development and epidemic prevention. The algorithms developed in this competition will also help address quantifying population statistics in regions of the world where Civil Registration Systems are not up-to-date, for which building footprints are a direct predictor. The high temporal cadence and planetary coverage of our Dove constellation enables an entirely new class of remote-sensing applications, including detecting rapid urbanization, updating maps in a diverse set of geographical areas (not just dense urban areas), detecting unplanned infrastructure development, finding illegal activities in protected areas and uncovering precursors to deforestation.įrom left to right: New buildings on the outskirts of Chengdu, China in March, May and July 2019 © 2019, Planet Labs Inc. Planet images the entire landmass of the Earth on a daily basis at 3-5 meter resolution. Using dense time-series imagery of over 100 regions around the globe, this competition will allow researchers to experiment with novel change detection methods that would be infeasible on smaller datasets. This technical challenge has been unlocked by access to a spatio-temporal dataset at a scale and cadence that was previously unavailable to the broader research community. Whereas all six previous SpaceNet challenges were based on static road and building detection, the partnership between SpaceNet and Planet will allow the seventh competition to focus on discovery of change events directly. Previous competitions led to a dramatic expansion of the availability of open source data of building footprints and road networks for the geospatial machine learning community. SpaceNet offers free, precision-labeled, electro-optical and synthetic aperture radar satellite imagery data sets and runs challenges with prizes to foster emerging analytical frameworks. Rapid and accurate remote-sensing of infrastructure change can aid in a variety of efforts, from infrastructure development to disaster preparedness to epidemic prevention.Įstablished in 2016 by In-Q-Tel’s CosmiQ Works, and DigitalGlobe (now part of Maxar Technologies), SpaceNet is dedicated to accelerating the research and application of open source AI technology for geospatial applications. The challenge focuses on developing better methods to track building construction over time using Planet imagery mosaics. We’re excited to partner with SpaceNet LLC, a nonprofit focused on machine learning techniques for geospatial applications, to support the SpaceNet7 Multi-Temporal Urban Development Challenge which was just recently announced. Planet Partners with SpaceNet for Multi-Temporal Urban Development Challenge. ![]()
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