UseGeo - A UAV-based multi-sensor dataset for geospatial research

Francesco Nex*, E.K Stathopoulou, F Remondino, Michael Yang, Logambal Madhuanand, Y Yogender, Alsadik B, M Weinmann, Jutzi B, Qin R

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

3D reconstruction is a long-standing research topic in the photogrammetric and computer vision communities; although a plethora of open-source and commercial solutions for 3D reconstruction have been released in the last few years, several open challenges and limitations still exist. Undoubtedly, deep learning algorithms have demonstrated great potential in several remote sensing tasks, including image-based 3D reconstruction. State-of-the-art monocular and stereo algorithms leverage deep learning techniques and achieve increased performance in depth estimation and 3D reconstruction. However, one of the limitations of such methods is that they highly rely on large training sets that are often tedious to obtain; even when available, they typically refer to indoor, close-range scenarios and low-resolution images. Especially while considering UAV (Unmanned Aerial Vehicle) scenarios, such data are not available and domain adaptation is not a trivial challenge. To fill this gap, the UAV-based multi-sensor dataset for geospatial research (UseGeo - https://usegeo.fbk.eu/home) is introduced in this paper. It contains both image and LiDAR data and aims to support relevant research in photogrammetry and computer vision with a useful training set for both stereo and monocular 3D reconstruction algorithms. In this regard, the dataset provides ground truth data for both point clouds and depth maps. In addition, UseGeo can be also a valuable dataset for other tasks such as feature extraction and matching, aerial triangulation, or image and LiDAR co-registration. The paper introduces the UseGeo dataset and validates some state-of-the-art algorithms to assess their usability for both monocular and multi-view 3D reconstruction.
Original languageEnglish
Article number100070
Number of pages12
JournalISPRS Open Journal of Photogrammetry and Remote Sensing
Volume13
Early online date18 Jun 2024
DOIs
Publication statusE-pub ahead of print - 18 Jun 2024

Keywords

  • 3D reconstruction
  • Deep learning
  • LiDAR
  • Monocular depth estimation
  • Multi-view stereo
  • Stereo matching
  • UAV

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