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dc.contributor.author
Xiao, Muyan
dc.contributor.author
Rothermel, Mathias
dc.contributor.author
Tom, Manu
dc.contributor.author
Galliani, Silvano
dc.contributor.author
Baltsavias, Emmanuel
dc.contributor.author
Schindler, Konrad
dc.contributor.editor
Remondino, Fabio
dc.contributor.editor
Toschi, I.
dc.contributor.editor
Fuse, T.
dc.date.accessioned
2019-10-10T09:50:58Z
dc.date.available
2018-06-22T13:44:16Z
dc.date.available
2018-07-09T15:28:15Z
dc.date.available
2019-10-10T09:50:58Z
dc.date.issued
2018
dc.identifier.issn
2194-9042
dc.identifier.issn
2194-9050
dc.identifier.other
10.5194/isprs-annals-IV-2-311-2018
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/271686
dc.identifier.doi
10.3929/ethz-b-000271686
dc.description.abstract
Continuous monitoring of climate indicators is important for understanding the dynamics and trends of the climate system. Lake ice has been identified as one such indicator, and has been included in the list of Essential Climate Variables (ECVs). Currently there are two main ways to survey lake ice cover and its change over time, in-situ measurements and satellite remote sensing. The challenge with both of them is to ensure sufficient spatial and temporal resolution. Here, we investigate the possibility to monitor lake ice with video streams acquired by publicly available webcams. Main advantages of webcams are their high temporal frequency and dense spatial sampling. By contrast, they have low spectral resolution and limited image quality. Moreover, the uncontrolled radiometry and low, oblique viewpoints result in heavily varying appearance of water, ice and snow. We present a workflow for pixel-wise semantic segmentation of images into these classes, based on state-of-the-art encoder-decoder Convolutional Neural Networks (CNNs). The proposed segmentation pipeline is evaluated on two sequences featuring different ground sampling distances. The experiment suggests that (networks of) webcams have great potential for lake ice monitoring. The overall per-pixel accuracies for both tested data sets exceed 95 %. Furthermore, per-image discrimination between ice-on and ice-off conditions, derived by accumulating per-pixel results, is 100 % correct for our test data, making it possible to precisely recover freezing and thawing dates.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Copernicus
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Climate Monitoring
en_US
dc.subject
Lake Ice Monitoring
en_US
dc.subject
Webcams
en_US
dc.subject
Semantic Segmentation
en_US
dc.subject
Convolutional Neural Networks
en_US
dc.title
Lake ice monitoring with webcams
en_US
dc.type
Conference Paper
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2018-05-28
ethz.journal.title
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ethz.journal.volume
IV-2
en_US
ethz.journal.abbreviated
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.
ethz.pages.start
311
en_US
ethz.pages.end
317
en_US
ethz.size
7 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.event
ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”
en_US
ethz.event.location
Riva del Garda, Italy
en_US
ethz.event.date
June 4-7, 2018
en_US
ethz.identifier.scopus
ethz.publication.place
Göttingen
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.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03886 - Schindler, Konrad / Schindler, Konrad
en_US
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
2018-06-22T13:44:26Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-07-09T15:28:23Z
ethz.rosetta.lastUpdated
2019-10-10T09:51:17Z
ethz.rosetta.versionExported
true
ethz.COinS
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