![]() Attention From my personal experience so far attention doesn’t make something worse, so here it helped too.A whole bunch of various encoders - SeNets were the most interesting ones.Polygons are ground truth Some other tricks have been tried Model and data processing Nadir=25 and nadir=34 with different azimuths. Postprocessing with borders mask and watershed.UResNeXt101(UNet + ResNeXt101) with transfer learning.The core of the task - identify all building footprints. The domain - satellite images of Atlanta suburb taken from different look angles( nadirs) separated into three groups: Nadir, Off Nadir, Very Off Nadir. In case you missed last year posts about participation in similar challenges: Spacenet three: Road detector and Crowd AI Mapping challenge Mistakes are easy to make anywhere and how fast you find and fix them defines your chances to win. Maybe the most important thing I have taught during this competition: Leaderboard is the only one truth. Honestly, many things went wrong, but I’ve got an enjoyable and useful experience and managed to improve my skills. What is the impact on performance for a multiclass feature extraction challenge - i.e.For me, Spacenet4 became the first serious DL competition.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). ![]()
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