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dc.contributor.author
Prabha, Rajanie
dc.contributor.author
Tom, Manu
dc.contributor.author
Rothermel, Mathias
dc.contributor.author
Baltsavias, Emmanuel
dc.contributor.author
Leal-Taixe, Laura
dc.contributor.author
Schindler, Konrad
dc.date.accessioned
2020-10-13T09:48:26Z
dc.date.available
2020-09-25T02:51:34Z
dc.date.available
2020-09-25T11:10:38Z
dc.date.available
2020-10-13T09:48:26Z
dc.date.issued
2020-08-03
dc.identifier.issn
2194-9042
dc.identifier.issn
2194-9050
dc.identifier.other
10.5194/isprs-annals-V-2-2020-549-2020
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/442469
dc.identifier.doi
10.3929/ethz-b-000442469
dc.description.abstract
© 2020 Copernicus GmbH. All rights reserved. Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatiooral information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, <i>Deeplab</i> v3+. Moreover, we design a variant of that model, termed <i>Deep-U-Lab</i>, which predicts sharper, more correct segmentation boundaries. We have tested the model's ability to generalise with data from multiple camera views and two different winters. On average, it achieves Intersection-over-Union (IoU) values of &asymp;71% across different cameras and &asymp;69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing websites. As part of the work, we introduce a new benchmark dataset of webcam images, <i>Photi-LakeIce</i>, from multiple cameras and two different winters, along with pixel-wise ground truth annotations.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Copernicus
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Lake Ice Monitoring with Webcams and Crowd-Sourced Images
en_US
dc.type
Conference Paper
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ethz.journal.volume
5
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.
ethz.pages.start
549
en_US
ethz.pages.end
556
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.event
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS 2020) (virtual)
ethz.event.location
Nice, France
ethz.event.date
September 30 - October 2, 2020
ethz.identifier.scopus
ethz.publication.place
Göttingen
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.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03886 - Schindler, Konrad / Schindler, Konrad
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.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03886 - Schindler, Konrad / Schindler, Konrad
ethz.date.deposited
2020-09-25T02:51:48Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-09-25T11:10:48Z
ethz.rosetta.lastUpdated
2024-02-02T12:18:08Z
ethz.rosetta.versionExported
true
ethz.COinS
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