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Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning | | Manu Tom
; Rajanie Prabha
; Tianyu Wu
; Emmanuel Baltsavias
; Laura Leal-Taixe
; Konrad Schindler
; | | Date: |
Tue, 27 Oct 2020 14:02:00 GMT (41809kb,D) | | Abstract: | Continuous observation of climate indicators, such as trends in lake
freezing, is important to understand the dynamics of the local and global
climate system. Consequently, lake ice has been included among the Essential
Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and
there is a need to set up operational monitoring capabilities. Multi-temporal
satellite images and publicly available webcam streams are among the viable
data sources to monitor lake ice. In this work we investigate machine
learning-based image analysis as a tool to determine the spatio-temporal extent
of ice on Swiss Alpine lakes as well as the ice-on and ice-off dates, from both
multispectral optical satellite images (VIIRS and MODIS) and RGB webcam images.
We model lake ice monitoring as a pixel-wise semantic segmentation problem,
i.e., each pixel on the lake surface is classified to obtain a spatially
explicit map of ice cover. We show experimentally that the proposed system
produces consistently good results when tested on data from multiple winters
and lakes. Our satellite-based method obtains mean Intersection-over-Union
(mIoU) scores >93%, for both sensors. It also generalises well across lakes and
winters with mIoU scores >78% and >80% respectively. On average, our webcam
approach achieves mIoU values of 87% (approx.) and generalisation scores of 71%
(approx.) and 69% (approx.) across different cameras and winters respectively.
Additionally, we put forward a new benchmark dataset of webcam images
(Photi-LakeIce) which includes data from two winters and three cameras. | | Source: | arXiv, 2010.14300 | | Services: | Forum | Review | PDF | Favorites | |
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