![]() The first network focuses on foundational features, which works by segmenting buildings and roads from the pre-event imagery without any flood attribution. Our baseline consists of two independently trained convolutional neural networks and post-processing steps to convert rasterized predictions into vector data suitable for submission. SpaceNet 8 participants may choose to modify the baseline or to develop an entirely new algorithm. The SpaceNet team also released a new baseline algorithm to provide a starting point for this challenge. In addition to the new training datasets provided for SpaceNet 8, prior SpaceNet datasets can be used as examples of building footprints and roads. aws s3 cp s3://spacenet-dataset/spacenet/SN8_floods/tarballs/Louisiana-West_Test_.aws s3 cp s3://spacenet-dataset/spacenet/SN8_floods/tarballs/Louisiana-East_Training_.aws s3 cp s3://spacenet-dataset/spacenet/SN8_floods/tarballs/Germany_Training_.aws s3 ls s3://spacenet-dataset/spacenet/SN8_floods/.The following commands can be used to download the training dataset: To download the data, you simply need a free Amazon Web Services (AWS) account and the AWS command line interface (CLI) installed and configured. The AOIs include Germany with flooding from heavy rains in July 2021, Louisiana following Hurricane Ida in August 2021, and a “mystery” location that will be used to test the top 10 algorithms from the public leaderboard for final scoring after the challenge has concluded. Along with the imagery, hand labeled building footprints, road and flood attributes are provided for training and scoring. Three areas of interest (AOIs) were selected for the dataset consisting of 12 Maxar satellite images of both pre- and post-flooding event imagery. This blog post provides more detail on each. Like past SpaceNet challenges, a new dataset and baseline algorithm are provided. This is the first SpaceNet dataset to also incorporate examples of such infrastructure affected by flooding. SpaceNet 8 expands on past infrastructure mapping tasks for building footprints and roads to a multiclass feature extraction and characterization problem. As explained in the previous blog post announcing SpaceNet 8, this challenge focuses on infrastructure and flood mapping related to hurricanes and heavy rains that cause route obstructions and significant damage. The SpaceNet 8 Challenge launched July 12 and is off to a great start with more than 180 participants registered via Topcoder in the first two weeks. ![]() The SpaceNet partners are proud to report the release of the SpaceNet 8 dataset and algorithmic baseline. ![]() SpaceNet is run by co-founder Maxar and our partners IEEE GRSS, Oak Ridge National Laboratory, Amazon Web Services (AWS) and Topcoder. The SpaceNet 8 Flood Detection Challenge: Dataset and Algorithmic Baseline ReleaseĮditor’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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