Abstract
Abstract. 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 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 websites. 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.
Highlights
Climate change is and will continue to be, a main challenge for humanity
All models are trained for 100 epochs with batch sizes of 4 for lake detection and 8 for lake ice segmentation, respectively
One conclusion that we drew from our study is that the previous, pioneering attempts (Xiao et al, 2018; Tom et al, 2019) underestimated the potential of deep convolutional networks for lake ice detection with webcams
Summary
Climate change is and will continue to be, a main challenge for humanity. One important piece of information about lakes in cooler climate zones are the times, duration and patterns of freezing and thawing. Webcams on lakes are a challenging outdoor scenario with limited image quality, and prone to unfavorable illumination, haze, etc; making it at times hard to distinguish between ice/snow or water, even for the human eye, see Fig. 2. We gathered and annotated several webcam streams. These include the data from four lakes and three summers for lake detection, and two lakes and two winters for lake ice segmentation. Entire data is curated and labelled by human annotators
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