Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019

Cheng, Daniel; Hayes, Wayne B.; Larour, Eric; Mohajerani, Yara; Wood, Michael; Velicogna, I.; Rignot, Eric

doi:10.5194/tc-15-1663-2021

Cited by 56 publications

(84 citation statements)

References 26 publications

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“…Our results build on the work of deep-learning-based classification methods for ice front delineation (Baumhoer et al, 2019;Mohajerani et al, 2019;Zhang et al, 2019;Cheng et al, 2021), with several key innovations and variations of note. Firstly, the CSC workflow produces multi-class outputs using seven semantic classes rather than the binary outputs of previous methods.…”

Section: Comparison To Previous Workmentioning

confidence: 74%

“…The second major difference between CSC and previous methods is the deep learning architecture. All previous deep learning classification methods for delineating ice fronts (Baumhoer et al, 2019;Mohajerani et al, 2019;Zhang et al, 2019;Cheng et al, 2021) use FCN/U-Net architectures (Ronneberger et al, 2015). Hoeser et al (2020) reviewed image segmentation and object detection in remote sensing, and whilst they do conclude that FCN/U-Net architectures are dominant, they still find about 30 % of published work uses patch-based approaches which are akin to the second phase of the CSC method presented here.…”

Section: Comparison To Previous Workmentioning

confidence: 99%

“…Moreover, given the simplicity of data pre-processing steps required for CSC, the workflow has good accessibility and can be implemented easily by new users. In contrast, for several of the previous studies which implement FCN architectures, a larger number of preprocessing steps are required, including but not limited to rotation for consistent glacier flow direction, edge enhancement, and pseudo-HDR toning (Mohajerani et al, 2019;Zhang et al, 2019;Cheng et al, 2021). Similarly, FCN architectures can be very demanding in terms of computer RAM and GPU RAM, especially when large images are used as inputs.…”

Section: Data Pre-processing and Computational Loadsmentioning

confidence: 99%

“…Semantic segmentation, a term interchangeable with pixel-level semantic classification, divides an image into its constituent parts based on groups of pixels of a given class and assigns each pixel a semantic label (Liu et al, 2019). It remains a core concept underlying more recent advancements which use deep learning approaches to classify imagery for more efficient automated calving front detection (Baumhoer et al, 2019;Mohajerani et al, 2019;Cheng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…maximum likelihood) produce more noisy classifications (Li et al, 2014). Previous studies which apply deep learning to detect the calving fronts of marineterminating glaciers used a type of CNN called a fully convolutional neural network (FCN) (Ronneberger et al, 2015) and various post-processing techniques to extract the boundaries between (1) ice and ocean in Antarctica (Baumhoer et al, 2019) and (2) marine-terminating outlet glaciers and mélange/water in Greenland (Mohajerani et al, 2019;Zhang et al, 2019;Cheng et al, 2021). Calving fronts detected using these methods deviate by 38 to 108 m (<2 to 6 px) from manual delineations, providing an accurate automated alternative to manual digitisation.…”

Section: Introductionmentioning

confidence: 99%

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Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods

2021

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Abstract. A wealth of research has focused on elucidating the key controls on mass loss from the Greenland and Antarctic ice sheets in response to climate forcing, specifically in relation to the drivers of marine-terminating outlet glacier change. The manual methods traditionally used to monitor change in satellite imagery of marine-terminating outlet glaciers are time-consuming and can be subjective, especially where mélange exists at the terminus. Recent advances in deep learning applied to image processing have created a new frontier in the field of automated delineation of glacier calving fronts. However, there remains a paucity of research on the use of deep learning for pixel-level semantic image classification of outlet glacier environments. Here, we apply and test a two-phase deep learning approach based on a well-established convolutional neural network (CNN) for automated classification of Sentinel-2 satellite imagery. The novel workflow, termed CNN-Supervised Classification (CSC) is adapted to produce multi-class outputs for unseen test imagery of glacial environments containing marine-terminating outlet glaciers in Greenland. Different CNN input parameters and training techniques are tested, with overall F1 scores for resulting classifications reaching up to 94 % for in-sample test data (Helheim Glacier) and 96 % for out-of-sample test data (Jakobshavn Isbrae and Store Glacier), establishing a state of the art in classification of marine-terminating glaciers in Greenland. Predicted calving fronts derived using optimal CSC input parameters have a mean deviation of 56.17 m (5.6 px) and median deviation of 24.7 m (2.5 px) from manually digitised fronts. This demonstrates the transferability and robustness of the deep learning workflow despite complex and seasonally variable imagery. Future research could focus on the integration of deep learning classification workflows with free cloud-based platforms, to efficiently classify imagery and produce datasets for a range of glacial applications without the need for substantial prior experience in coding or deep learning.

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Section: Comparison To Previous Workmentioning

confidence: 74%