Research data associated with paper: A Semantic Segmentation Model for Lumbar MRI Images using Divergence Loss, comprising the python code, a trained model and empirical results. 


The Contest: Goals and Organization

The 2022 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee, aims to promote research on semi-supervised learning. The overall objective is to build models that are able to leverage a large amount of unlabelled data while only requiring a small number of annotated training samples. The 2022 Data Fusion Contest will consist of two challenge tracks:

Track SLM:Semi-supervised Land Cover Mapping

Last Updated On: 
Thu, 01/13/2022 - 17:04

The dataset contains UAV imagery and fracture interpretation of rock outcrops acquired in Praia das Conchas, Cabo Frio, Rio de Janeiro, Brazil. Along with georeferenced .geotiff images, the dataset contains filtered 500 x 500 .png tiles containing only scenes with fracture data, along with .png binary masks for semantic segmentation and original georeferenced shapefile annotations. This data can be useful for segmentation and extraction of geological structures from UAV imagery, for evaluating computer vision methodologies or machine learning techniques.


This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile 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. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.


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:

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:

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:

SpaceNet 4 source data:

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:

Pairwise semantic stereo:

Multi-view semantic stereo:

3D point cloud classification:


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.


Endoscopy is a widely used clinical procedure for the early detection of cancers in hollow-organs such as oesophagus, stomach, and colon. Computer-assisted methods for accurate and temporally consistent localisation and segmentation of diseased region-of-interests enable precise quantification and mapping of lesions from clinical endoscopy videos which is critical for monitoring and surgical planning. Innovations have the potential to improve current medical practices and refine healthcare systems worldwide.

Last Updated On: 
Sat, 02/27/2021 - 05:11