What is the impact on performance for a multiclass feature extraction challenge-i.e. 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: The data is composed of multispectral images of cities (200m x 200m patches) with corresponding ground-truth. Any winning open-source algorithm from SpaceNet 1-7 may also be used. The SpaceNet dataset is a body of 17355 images collected from DigitalGlobe’s WorldView-2 (WV-2) and WorldView-3 (WV-3) multispectral imaging satellites and has been released as a collaboration of DigialGlobe, CosmiQ Works and NVIDIA. 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 competition centers around a new open source dataset of Planet satellite imagery mosaics, which includes 24 images (one per month) covering 100 unique. The goal of SpaceNet 8 is to leverage the existing repository of datasets and algorithms from SpaceNet Challenges 1-7 and apply them to a real-world disaster response scenario, expanding to multiclass feature extraction and characterization. To help address this need, 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. Each year, natural disasters such as hurricanes, tornadoes, earthquakes and floods significantly damage infrastructure and result in loss of life, property and billions of dollars.
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