Urban Semantic 3D Dataset

Detailed information about the data content, organization, and file formats is provided in the README files. For image data, individual TAR files for training and validation are provided for each city. Extra training data is also provided in separate TAR files. For point cloud data, individual ZIP files are provided for each city from DFC19. These include the original DFC19 training and validation point clouds with full UTM coordinates to enable experiments requiring geolocation.

Original DFC19 dataset:

https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019

We added new reference data to this extended US3D dataset to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. For details, please see our CVPR paper.

CVPR paper on geocentric pose:

http://openaccess.thecvf.com/content_CVPR_2020/papers/Christie_Learning_...

Source Data Attribution

All data used to produce the extended US3D dataset is publicly sourced. Data for DFC19 was derived from public satellite images released for IARPA CORED. New data for Atlanta was derived from public satellite images released for SpaceNet 4, licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. All other commercial satellite images were provided courtesy of DigitalGlobe. U. S. Cities LiDAR and vector data were made publicly available by the Homeland Security Infrastructure Program. 

CORE3D source data: https://spacenetchallenge.github.io/datasets/Core_3D_summary.html

SpaceNet 4 source data: https://spacenetchallenge.github.io/datasets/spacenet-OffNadir-summary.html

Test Sets

Validation data from DFC19 is extended here to include additional data for each tile. Test data is not provided for the DFC19 cities or for Atlanta. Test sets are available for the DFC19 challenge problems on CodaLab leaderboards. We plan to make test sets for all cities available for the geocentric pose problem in the near future. 

Single-view semantic 3D: https://competitions.codalab.org/competitions/20208

Pairwise semantic stereo: https://competitions.codalab.org/competitions/20212

Multi-view semantic stereo: https://competitions.codalab.org/competitions/20216

3D point cloud classification: https://competitions.codalab.org/competitions/20217

References

If you use the extended US3D dataset, please cite the following papers:

G. Christie, K. Foster, S. Hagstrom, G. D. Hager, and M. Z. Brown, "Single View Geocentric Pose in the Wild," Proc. of Computer Vision and Pattern Recognition EarthVision Workshop, 2021.

G. Christie, R. Munoz, K. Foster, S. Hagstrom, G. D. Hager, and M. Z. Brown, "Learning Geocentric Object Pose in Oblique Monocular Images," Proc. of Computer Vision and Pattern Recognition, 2020.

B. Le Saux, N. Yokoya, R. Hansch, and M. Brown, "2019 IEEE GRSS Data Fusion Contest: Large-Scale Semantic 3D Reconstruction [Technical Committees]", IEEE Geoscience and Remote Sensing Magazine, 2019.

M. Bosch, K. Foster, G. Christie, S. Wang, G. D. Hager, and M. Brown, "Semantic Stereo for Incidental Satellite Images," Proc. of Winter Applications of Computer Vision, 2019.