SpaceNet: Accelerating geospatial machine learning MVS Dataset | SpaceNet on AWS
IARPA CORE3D Public Data Urban 3D Challenge Dataset fMoW Dataset MVS Dataset SpaceNet Off-Nadir Dataset SpaceNet Buildings Dataset V1 SpaceNet Buildings Dataset V2 SpaceNet Imagery SpaceNet POI Dataset SpaceNet Roads Dataset

IARPA Multi-View Stereo 3D Mapping Challenge

The availability of public multiple view stereo (MVS) benchmark datasets has been instrumental in enabling research to advance the state of the art in the field and to apply and customize methods to real-world problems. In this work, we provide a public benchmark data set for multiple view stereo applied to 3D outdoor scene mapping using commercial satellite imagery.

This data set includes DigitalGlobe WorldView-3 panchromatic and multispectral images of a 100 square kilometer area near San Fernando, Argentina. We also provide 20cm airborne lidar ground truth data for a 20 square kilometer subset of this area and performance analysis software to assess accuracy and completeness metrics. Commercial satellite imagery is provided courtesy of DigitalGlobe, and ground truth lidar is provided courtesy of IARPA.

This data supported the IARPA Multi-View Stereo 3D Mapping Challenge and is now made publicly available with no restrictions to support continued research. JHU/APL does not plan to maintain an online benchmark leaderboard, but we welcome your feedback and would love to hear about what you’re doing with the data and include your published results on this page.

SpaceNet is hosting the Multi-View Stereo 3D Mapping dataset in the spacenet repository to ensure easy access to the data.

Competition Websites

For more information about the IARPA Competition, Please visit the Multi-View Stereo 3D Mapping Challenge Website

For more information about the MVS benchmark please visit the JHUAPL competition webpage

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


aws s3 ls s3://spacenet-dataset/mvs_dataset 

The catalog contains the following packages:

Software Code with regard to this dataset:

Available solutions from contest winners:

Published Papers

Published results:

  1. G. Facciolo, C. de Franchis, E. Meinhardt-Llopis, “Automatic 3D Reconstruction from Multi-Date Satellite Images,” IEEE International Conference on Computer Vision and Pattern Recognition, EARTHVISION Workshop, 2017.

  2. R. Qin, “Automated 3D recovery from very high resolution multi-view images,” ASPRS 2017 Annual Conference, 2017.


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