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dc.contributor.author
Qin, Rongjun
dc.contributor.author
Grün, Armin
dc.date.accessioned
2021-03-02T06:44:40Z
dc.date.available
2020-08-23T02:34:43Z
dc.date.available
2020-09-02T07:35:33Z
dc.date.available
2021-03-02T06:44:40Z
dc.date.issued
2021
dc.identifier.issn
1753-8947
dc.identifier.issn
1753-8955
dc.identifier.other
10.1080/17538947.2020.1805037
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/432243
dc.identifier.doi
10.3929/ethz-b-000432243
dc.description.abstract
The process of modern photogrammetry converts images and/or LiDAR data into usable 2D/3D/4D products. The photogrammetric industry offers engineering-grade hardware and software components for various applications. While some components of the data processing pipeline work already automatically, there is still substantial manual involvement required in order to obtain reliable and high-quality results. The recent development of machine learning techniques has attracted a great attention in its potential to address complex tasks that traditionally require manual inputs. It is therefore worth revisiting the role and existing efforts of machine learning techniques in the field of photogrammetry, as well as its neighboring field computer vision. This paper provides an overview of the state-of-the-art efforts in machine learning in bringing the automated and 'intelligent' component to photogrammetry, computer vision and (to a lesser degree) to remote sensing. We will primarily cover the relevant efforts following a typical 3D photogrammetric processing pipeline: (1) data acquisition (2) geo-referencing/interest point matching (3) Digital Surface Model generation (4) semantic interpretations, followed by conclusions and our insights.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Taylor & Francis
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Photogrammetry
en_US
dc.subject
camera calibration
en_US
dc.subject
3D modeling
en_US
dc.subject
machine learning
en_US
dc.subject
object recognition
en_US
dc.subject
semantic interpretation
en_US
dc.title
The role of machine intelligence in photogrammetric 3D modeling - an overview and perspectives
en_US
dc.type
Review Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2020-08-10
ethz.journal.title
International Journal of Digital Earth
ethz.journal.volume
14
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
15
en_US
ethz.pages.end
31
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2020-08-23T02:34:49Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-02T06:44:50Z
ethz.rosetta.lastUpdated
2021-03-02T06:44:50Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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