# A New Stereo Benchmarking Dataset for Satellite Images

arXiv: https://arxiv.org/abs/1907.04404 · HTML: https://mlsanity.com/paper/1907.04404/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 15 of 15)

- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite Images](https://mlsanity.com/paper/2106.08107/) (2021)
- [ImpliCity: City Modeling from Satellite Images with Deep Implicit Occupancy Fields](https://mlsanity.com/paper/2201.09968/) (2022)
- [WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images](https://mlsanity.com/paper/2206.02342/) (2022)
- [Incorporating Season and Solar Specificity into Renderings made by a NeRF Architecture using Satellite Images](https://mlsanity.com/paper/2308.01262/) (2023)
- [LiDAR-guided Stereo Matching with a Spatial Consistency Constraint](https://mlsanity.com/paper/2202.09953/) (2022)
- [A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images](https://mlsanity.com/paper/2210.14031/) (2022)
- [Cumulative Assessment for Urban 3D Modeling](https://mlsanity.com/paper/2107.04622/) (2021)
- [UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios](https://mlsanity.com/paper/2302.10082/) (2023)
- [DeepSim-Nets: Deep Similarity Networks for Stereo Image Matching](https://mlsanity.com/paper/2304.08056/) (2023)
- [SatDepth: A Novel Dataset for Satellite Image Matching](https://mlsanity.com/paper/2503.12706/) (2025)
- [Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching Networks](https://mlsanity.com/paper/2408.07613/) (2024)
- [Multi-view dense image matching with similarity learning and geometry priors](https://mlsanity.com/paper/2505.11264/) (2025)
- [EpiMask: Leveraging Epipolar Distance Based Masks in Cross-Attention for Satellite Image Matching](https://mlsanity.com/paper/2603.21463/) (2026)
- [SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI](https://mlsanity.com/paper/2607.27139/) (2026)

## Abstract (arXiv)

In order to facilitate further research in stereo reconstruction with multi-date satellite images, the goal of this paper is to provide a set of stereo-rectified images and the associated groundtruthed disparities for 10 AOIs (Area of Interest) drawn from two sources: 8 AOIs from IARPA's MVS Challenge dataset and 2 AOIs from the CORE3D-Public dataset. The disparities were groundtruthed by first constructing a fused DSM from the stereo pairs and by aligning 30 cm LiDAR with the fused DSM. Unlike the existing benckmarking datasets, we have also carried out a quantitative evaluation of our groundtruthed disparities using human annotated points in two of the AOIs. Additionally, the rectification accuracy in our dataset is comparable to the same in the existing state-of-the-art stereo datasets. In general, we have used the WorldView-3 (WV3) images for the dataset, the exception being the UCSD area for which we have used both WV3 and WorldView-2 (WV2) images. All of the dataset images are now in the public domain. Since multi-date satellite images frequently include images acquired in different seasons (which creates challenges in finding corresponding pairs of pixels for stereo), our dataset also includes for each image a building mask over which the disparities estimated by stereo should prove reliable. Additional metadata included in the dataset includes information about each image's acquisition date and time, the azimuth and elevation angles of the camera, and the intersection angles for the two views in a stereo pair. Also included in the dataset are both quantitative and qualitative analyses of the accuracy of the groundtruthed disparity maps. Our dataset is available for download at urlhttps://engineering.purdue.edu/RVL/Database/SatStereo/index.html