Shadow Neural Radiance Fields for Multi-View Satellite Photogrammetry - Dataset

<p>Data accompanying the paper Derksen, Dawa, and Dario Izzo. &quot;Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry.&quot; <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em>. 2021.</p> <p>This dataset contains a subset of the World-View-3 images from the IEEE Data Fusion Competition 2019 - Track 3, downloaded from https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019.</p> <p>It is organized in four folders, one for each study area, following the original area names. Each folder contains a multi-view set of RGB images, cropped to the validation area, and rotated according to the azimuth angle. The folder also contains a Digital Surface Model of the area (DSM) as a one-band .tif file where the values contained in pixels represent the surface altitude in meters. Finally the folder contains a &quot;metadata&quot; file which provides for each image ID the radius (distance from satellite to scene), as well as the viewing and lighting directions (azimuth and elevation).</p> <p>The authors would like to thank the Johns Hopkins University Applied Physics Laboratory and IARPA for providing the data used in this study, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee for organizing the Data Fusion Contest.</p>