- Conference Paper
Rights / licenseCreative Commons Attribution 4.0 International
© 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 ≈71% across different cameras and ≈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. Show more
Journal / seriesISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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