  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 spatio-temporal 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 different lakes, with a
state-of-the-art Convolutional Neural Network (CNN) model for semantic image
segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed
Deep-U-Lab, 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 web sites. As part of the work, we introduce a new benchmark
dataset of webcam images, Photi-LakeIce, from multiple cameras and two
different winters, along with pixel-wise ground truth annotations.
