Steve Wenrich

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 . 


  • 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/



Creation of Operationally Realistic 3D Environment (CORE3D)

IARPA has publicly released DigitalGlobe satellite imagery for the Creation of Operationally Realistic 3D Environment (CORE3D) program to enable performer teams to crowdsource manual labeling efforts and to promote public research that aligns well with the CORE3D program’s objectives.

​SpaceNet is hosting the CORE3D public dataset in the SpaceNet repository to ensure easy access to the data.

Reference Requirement

Please reference the following when reporting results using any of this data:

  • M. Brown, H. Goldberg, K. Foster, A. Leichtman, S. Wang, S. Hagstrom, M. Bosch, and S. Almes, LargeScale Public Lidar and Satellite Image Data Set for Urban Semantic Labeling, in Proc. SPIE Laser Radar Technology and Applications XXII, 2018.
  • Commercial satellite imagery in the CORE3D public dataset was provided courtesy of DigitalGlobe.
  • Dataset was created for the IARPA CORE3D program:
  • SpaceNet on Amazon Web Services (AWS). Datasets. The SpaceNet Catalog. Last modified October 15, 2018. Accessed on [Insert Date].


aws s3 ls s3://spacenet-dataset/Hosted-Datasets/CORE3D-Public-Data/
  • ​One WorldView-2 PAN and MSI image for Jacksonville, FL; Tampa, FL; Richmond, VA; and Omaha, NE
  • Tiled WorldView-2 data sets including ground truth building labels for comparison with the USSOCOM Urban 3D Challenge
  • 26 WorldView-3 PAN and MSI images over Jacksonville, FL
  • 43 WorldView-3 PAN and MSI images over Omaha, NE
  • 35 WorldView-3 PAN and MSI images over UCSD, CA
  • 44 WorldView-2 PAN and MSI images over UCSD, CAß
  • See the referenced SPIE paper for information about where to find corresponding lidar and other complementary data sets for each location
  • For images over San Fernando, Argentina for the IARPA Multi-View Stereo 3D Mapping Challenge, see


The AWS Command Line Interface (CLI) must be installed with an active AWS account. Configure the AWS CLI using ‘aws configure’

Questions and Comments

For questions and comments about the dataset or the open source software, please contact pubgeo(at)jhuapl(dot)edu.