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dc.contributor.author
Farmakis-Serebryakova, Marianna
dc.contributor.author
Heitzler, Magnus
dc.contributor.author
Hurni, Lorenz
dc.date.accessioned
2022-08-11T14:17:09Z
dc.date.available
2022-08-09T16:39:23Z
dc.date.available
2022-08-11T14:17:09Z
dc.date.issued
2022-07
dc.identifier.issn
2220-9964
dc.identifier.other
10.3390/ijgi11070395
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/562706
dc.identifier.doi
10.3929/ethz-b-000562706
dc.description.abstract
Since landforms composing land surface vary in their properties and appearance, their shaded reliefs also present different visual impression of the terrain. In this work, we adapt a U-Net so that it can recognize a selection of landforms and can segment terrain. We test the efficiency of 10 separate models and apply an ensemble approach, where all the models are combined to potentially outperform single models. Our algorithm works particularly well for block mountains, Prealps, valleys, and hills, delivering average precision and f1 values above 60%. Segmenting plateaus and folded mountains is more challenging, and their precision values are rather scattered due to smaller areas available for training. Mountains formed by erosion processes are the least recognized landform of all because of their similarities with other landforms. The highest accuracy of one of the 10 models is 65%, while the accuracy of the ensemble is 61%. We apply relief shading techniques that were found to be efficient regarding specific landforms within corresponding segmented areas and blend them together. Finally, we test the trained model with the best accuracy on other mountainous areas around the world, and it proves to work in other regions beyond the training area.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
landform recognition
en_US
dc.subject
convolutional neural networks
en_US
dc.subject
relief shading
en_US
dc.subject
geomorphology
en_US
dc.subject
machine learning
en_US
dc.subject
geoAI
en_US
dc.title
Terrain Segmentation Using a U-Net for Improved Relief Shading
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2022-07-12
ethz.journal.title
ISPRS International Journal of Geo-Information
ethz.journal.volume
11
en_US
ethz.journal.issue
7
en_US
ethz.journal.abbreviated
ISPRS int. j. geo-inf.
ethz.pages.start
395
en_US
ethz.size
19 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02648 - Inst. f. Kartografie und Geoinformation / Institute of Cartography&Geoinformation::03466 - Hurni, Lorenz / Hurni, Lorenz
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02648 - Inst. f. Kartografie und Geoinformation / Institute of Cartography&Geoinformation::03466 - Hurni, Lorenz / Hurni, Lorenz
ethz.date.deposited
2022-08-09T16:41:07Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-08-11T14:17:17Z
ethz.rosetta.lastUpdated
2023-02-07T05:14:21Z
ethz.rosetta.versionExported
true
ethz.COinS
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