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Flood Vulnerability Detection Challenge Using Multiclass Segmentation

SN8: Flood Detection Challenge Using Multiclass Segmentation 

The Problem

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. 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. 

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. 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. 

Since its launch in 2016, SpaceNet has made significant progress advancing open-source building footprint and road extraction algorithms. 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. Any winning open-source algorithm from SpaceNet 1-7 may also be used. 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. 

SpaceNet 8 aims to answer these questions:

  • How have algorithms that extract buildings and roads improved since SpaceNet was launched, and how can top algorithms from previous challenges be leveraged? 
  • What is the impact on performance for a multiclass feature extraction challenge—i.e., buildings and roads? 
  • How accurately can roads obstructed by flood waters be characterized from pre-event road detections and post-event satellite imagery? 

Training Data

aws s3 cp  s3://spacenet-dataset/spacenet/SN8_floods/tarballs/Germany_Training_Public.tar.gz . 
aws s3 cp s3://spacenet-dataset/spacenet/SN8_floods/tarballs/Louisiana-East_Training_Public.tar.gz . 

Testing Data

aws s3 cp  s3://spacenet-dataset/spacenet/SN8_floods/tarballs/Louisiana-West_Test_Public.tar.gz . 

Catalog

  • The data is hosted on AWS as a Public Dataset. It is free to download. An AWS account is not required; however, you must have the AWS CLI installed to access the data.  
  • To explore the dataset: 
    aws s3 ls s3://spacenet-dataset/spacenet/SN8_floods/

License

Las Vegas

Las Vegas

SpaceNet AOI 2 – Las Vegas

Catalog ID: 10400100137F4900
Image Time: 2015-10-22T18:36:56Z

Download Instructions

To view the contents of the dataset

aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_2_Vegas_geojson_roads_speed.tar.gz . aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_2_Vegas.tar.gz . aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/SN2_buildings_train_AOI_2_Vegas.tar.gz .

SpaceNet Simple Storage Service (S3) Directory Structure (AOI 2)

AOIs
└── AOI_2_Vegas
    ├── MS             # Raw source geotiffs of 8-Band Multi-Spectral raster data from WorldView-3
    ├── PS-MS          # Raw source geotiffs and COGs of 8-Band Multi-Spectral raster data pansharpened to 0.3m
    ├── PAN            # Raw source geotiffs of Panchromatic raster data from Worldview-3
    ├── PS-RGB         # Raw source geotiffs of RGB raster data from Worldview-3 pansharpened to 0.3m
    ├── metadata       # Collect metadata in .XML format
    └── misc	       # SpaceNet 2 challenge tarballs

 

Las Vegas // Roads Dataset Resources

AOI 2 – Vegas – Road Network Extraction Training

To download processed 400mx400m tiles of AOI 2 (25 GB) with associated building footprints for training do the following:

aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_train_AOI_2_Vegas.tar.gz . 

AOI 2 – Vegas – Road Network Extraction Testing

To download processed 400mx400m tiles of AOI 2 (8.1 GB) for testing do:

aws s3 cp s3://spacenet-dataset/spacenet/SN3_roads/tarballs/SN3_roads_test_public_AOI_2_Vegas.tar.gz . 

Las Vegas // Buildings Dataset Resources

AOI 2 – Vegas – Building Extraction Training

To download processed 200mx200m tiles of AOI 2 (23 GB) with associated building footprints for training do the following:

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/SN2_buildings_train_AOI_2_Vegas.tar.gz . 

AOI 2 – Vegas – Building Extraction Testing

To download processed 200mx200m tiles of AOI 2 (7.9 GB) for testing do:

aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/AOI_2_Vegas_test_public.tar.gz .