UAVid-depth Dataset

DOI

The UAVid-depth dataset is created from public UAVid dataset (Lyu et al. 2020) which is a high-resolution UAV semantic segmentation dataset focusing on street scenes. The UAVid dataset consists video sequences taken from Germany and China which are captured with 4K high-resolution in oblique views. For UAVid-depth dataset, the original video is converted to images at a frame rate of 5 images per second for Germnay dataset and 1 image per second for China. The reference depth for some of the test images are provided in the UAVid-depth dataset.Task DescriptionSelf-supervised monocular depth estimation from UAV videos.The original video files for each sequence will be provided upon request, or are available for download at UAVid homepage (https://www.uavid.nl/).Data DescriptionTraining data, validation data and test data are provided for Germany and China dataset separately. The data is arranged in the folders based on the captured video files.Reference DepthThe reference depth for some of the test images (three sequences) are generated from the photogrammetric point clouds and are uploaded along with the corresponding test sequence.SMDE Model DepthThe depth for some of the test images (three sequences) are generated from SMDE model (Madhuanand et al. 2021) and are uploaded along with the corresponding test sequence.CopyrightUAVid-depth dataset is copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.CitationWhen using this UAVid-depth dataset in your research, please cite:@article{uaviddepth21,Author = {Logambal Madhuanand and Francesco Nex and Michael Ying Yang},Title = {Self-supervised monocular depth estimation from oblique UAV videos},journal = {ISPRS Journal of Photogrammetry and Remote Sensing},year = {2021},volume = {176},pages = {1-14},}When using the UAVid dataset in your research, please cite:@article{uavid20,Author = {Ye Lyu andGeorge Vosselman andGuisong Xia andAlper Yilmaz andMichael Ying Yang},Title = {UAVid: A Semantic Segmentation Dataset for UAV Imagery},journal = {ISPRS Journal of Photogrammetry and Remote Sensing},year = {2020},}*Contactmichael.yang@utwente.nl

Date Submitted: 2021-05-28

Files not yet migrated to Data Station. Files for this dataset can be found at https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:210775.

Identifier
DOI https://doi.org/10.17026/dans-zux-xqv4
Metadata Access https://phys-techsciences.datastations.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.17026/dans-zux-xqv4
Provenance
Creator M.Y. Yang ORCID logo
Publisher DANS Data Station Phys-Tech Sciences
Contributor M Th Koelen; L. Madhuanand (Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente); F. Nex (Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente)
Publication Year 2021
Rights CC-BY-SA-4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by-sa/4.0
OpenAccess true
Contact M Th Koelen (Faculty of Geo-Information Science and Earth Observation)
Representation
Resource Type Dataset
Format application/zip
Size 19187
Version 1.0
Discipline Earth and Environmental Science; Environmental Research; Geosciences; Natural Sciences