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Unmanned aerial image dataset: Ready for 3D reconstruction

Unmanned aerial vehicles (UAVs) have become popular platforms for collecting various types of geospatial data for various mapping, monitoring and modelling applications. With the advancement of imaging and computing technologies, a vast variety of photogrammetric, computer-vision and, nowadays, end-...

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Autores principales: Shahbazi, Mozhdeh, Ménard, Patrick, Sohn, Gunho, Théau, Jérome
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6554229/
https://www.ncbi.nlm.nih.gov/pubmed/31194095
http://dx.doi.org/10.1016/j.dib.2019.103962
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author Shahbazi, Mozhdeh
Ménard, Patrick
Sohn, Gunho
Théau, Jérome
author_facet Shahbazi, Mozhdeh
Ménard, Patrick
Sohn, Gunho
Théau, Jérome
author_sort Shahbazi, Mozhdeh
collection PubMed
description Unmanned aerial vehicles (UAVs) have become popular platforms for collecting various types of geospatial data for various mapping, monitoring and modelling applications. With the advancement of imaging and computing technologies, a vast variety of photogrammetric, computer-vision and, nowadays, end-to-end learning workflows are introduced to produce three-dimensional (3D) information in form of digital surface and terrain models, textured meshes, rectified mosaics, CAD models, etc. These 3D products might be used in applications where accuracy and precision play a vital role, e.g. structural health monitoring. Therefore, extensive tests against data with relevant characteristics and reliable ground-truth are required to assess and ensure the performance of 3D modelling workflows. This article describes the images collected by a customized unmanned aerial vehicle (UAV) system from an open-pit gravel mine accompanied with additional data that will allow implementing and evaluating any structure-from-motion or photogrammetric approach for sparse or dense 3D reconstruction. This dataset includes total of 158 high-quality images captured with more than 80% endlap and spatial resolution higher than 1.5 cm, the 3D coordinates of 109 ground control points and checkpoints, 2D coordinates of more than 40K corresponding points among the images, a subset of 25 multi-view stereo images selected from an area of approximately 30 m × 40 m within the scene accompanied with a dense point cloud measured by a terrestrial laser scanner.
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spelling pubmed-65542292019-06-10 Unmanned aerial image dataset: Ready for 3D reconstruction Shahbazi, Mozhdeh Ménard, Patrick Sohn, Gunho Théau, Jérome Data Brief Computer Science Unmanned aerial vehicles (UAVs) have become popular platforms for collecting various types of geospatial data for various mapping, monitoring and modelling applications. With the advancement of imaging and computing technologies, a vast variety of photogrammetric, computer-vision and, nowadays, end-to-end learning workflows are introduced to produce three-dimensional (3D) information in form of digital surface and terrain models, textured meshes, rectified mosaics, CAD models, etc. These 3D products might be used in applications where accuracy and precision play a vital role, e.g. structural health monitoring. Therefore, extensive tests against data with relevant characteristics and reliable ground-truth are required to assess and ensure the performance of 3D modelling workflows. This article describes the images collected by a customized unmanned aerial vehicle (UAV) system from an open-pit gravel mine accompanied with additional data that will allow implementing and evaluating any structure-from-motion or photogrammetric approach for sparse or dense 3D reconstruction. This dataset includes total of 158 high-quality images captured with more than 80% endlap and spatial resolution higher than 1.5 cm, the 3D coordinates of 109 ground control points and checkpoints, 2D coordinates of more than 40K corresponding points among the images, a subset of 25 multi-view stereo images selected from an area of approximately 30 m × 40 m within the scene accompanied with a dense point cloud measured by a terrestrial laser scanner. Elsevier 2019-05-24 /pmc/articles/PMC6554229/ /pubmed/31194095 http://dx.doi.org/10.1016/j.dib.2019.103962 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Computer Science
Shahbazi, Mozhdeh
Ménard, Patrick
Sohn, Gunho
Théau, Jérome
Unmanned aerial image dataset: Ready for 3D reconstruction
title Unmanned aerial image dataset: Ready for 3D reconstruction
title_full Unmanned aerial image dataset: Ready for 3D reconstruction
title_fullStr Unmanned aerial image dataset: Ready for 3D reconstruction
title_full_unstemmed Unmanned aerial image dataset: Ready for 3D reconstruction
title_short Unmanned aerial image dataset: Ready for 3D reconstruction
title_sort unmanned aerial image dataset: ready for 3d reconstruction
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6554229/
https://www.ncbi.nlm.nih.gov/pubmed/31194095
http://dx.doi.org/10.1016/j.dib.2019.103962
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