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Harvard Medical School and the Laboratory for Innovation Science at Harvard, the Northumbrian Water Group, SpaceNet, Zurich Insurance Group and other global organizations that use the Topcoder Platform for on-demand access to top IT and data science talent, report high quality results.

Topcoder Platform enhancements empower companies with unprecedented ways to process and secure data, increase analytics speed, and avoid costly recruitment and retention challenges associated with IT staffing. Highlights include native GPU support and the ability to develop advanced analytic solutions with any tool, library or cloud application service.

What is the impact on performance for a multiclass feature extraction challenge - i.e.BOSTON & BANGALORE, India-( BUSINESS WIRE)- Topcoder, a Wipro company, and the world’s largest technology network and on-demand digital talent platform, today announced the addition of new data science and AI features to the Topcoder Platform.

How have algorithms that extract buildings and roads improved since SpaceNet was launched, and how can top algorithms from previous challenges be leveraged?.

SpaceNet 8 aims to answer these questions: New areas of interest (AOIs) will include New Orleans, Louisiana, following Hurricane Ida in August 2021 Dernau, Germany, during the June 2021 floods across Western Europe and a new “mystery city” for blind testing of the algorithms. Any winning open-source algorithm from SpaceNet 1–7 may also be used. During SpaceNet 8, challenge participants will train algorithms on imagery and labels from previous challenges - as well as newly created labeled training datasets from Maxar - to rapidly map an area affected by flooding. Since its launch in 2016, SpaceNet has made significant progress advancing open-source building footprint and road extraction algorithms. The SpaceNet 8 Flood Detection Challenge will focus on infrastructure and flood mapping related to hurricanes and heavy rains that cause route obstructions and significant damage.

This challenge also expands the task to a multiclass feature extraction and characterization problem. The goal of SpaceNet 8 is to leverage both existing datasets and algorithms from SpaceNet Challenges 1–7 as well as new training data and a baseline algorithm, then apply them to a real-world disaster response scenario. With this need in mind, the SpaceNet 8 Flood Detection Challenge will focus on infrastructure and flood mapping related to hurricanes and heavy rains that cause route obstructions and significant damage. As these events become more frequent and severe, there is an increasing need to rapidly develop maps and analyze the scale of destruction to better direct resources and first responders. SpaceNet is run by co-founder Maxar and our partners Amazon Web Services (AWS), IEEE-GRSS, Oak Ridge National Laboratory and Topcoder.Įach year, natural disasters such as hurricanes, tornadoes, earthquakes and floods significantly damage infrastructure and result in loss of life, property and billions of dollars. Announcing SpaceNet 8: Flood Detection Challenge Using Multiclass SegmentationĮditor’s note: SpaceNet is an initiative dedicated to accelerating open-source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e., building footprint and road network detection).