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Glaciers have experienced a global trend of recession within the past century. Quantification of glacier variations using satellite imagery has been of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. Spatiotemporal glacier dynamics must be monitored to quantify glacier variations. The potential methods to quantify spatiotemporal glacier dynamics with increasing complexity levels include detecting the terminus location, measuring the length of the glacier from the accumulation zone to the terminus, quantifying the glacier surface area, and measuring glacier volume. Although some deep learning methods designed purposefully for glacier boundary segmentation have achieved acceptable results, these models are often localized to the region where their training data were acquired and further rely on the training sets that were often curated manually to highlight glacial regions. Due to the very large number of glaciers, it is practically impossible to perform a worldwide study of glacier dynamics using manual methods. As a result, an automated or semi-automated method is highly desirable. The current study has built upon our previous works moving towards identification methods of the 2D glacier profile for glacier area segmentation. In this study, a deep learning method is proposed for segmentation of temporal Landsat images to quantify the glacial region within the Mount Cook/Aoraki massif located in the Southern Alps/Kā Tiritiri o te Moana of New Zealand/Aotearoa. Segmented glacial regions can be further utilized to determine the relationship of their variations due to climate change. This model has demonstrated promising performance while trained on a relatively small dataset. The permanent ice and snow class was accurately segmented at a 92% rate by the proposed model. 

Abstract In 2010, Turaev introduced knotoids as a variation on knots that replaces the embedding of a circle with the embedding of a closed interval with two endpoints which here we call poles. We define generalised knotoids to allow arbitrarily many poles, intervals and circles, each pole corresponding to any number of interval endpoints, including zero. This theory subsumes a variety of other related topological objects and introduces some particularly interesting new cases. We explore various analogs of knotoid invariants, including height, index polynomials, bracket polynomials and hyperbolicity. We further generalise to knotoidal graphs, which are a natural extension of spatial graphs that allow both poles and vertices.