GlacierNet: A Deep-Learning Approach for Debris-Covered Glacier Mapping

Xie, Zhiyuan; Haritashya, U. K.; Asari, Vijayan K.; Young, Brennan W.; Bishop, Michael P.; Kargel, Jeffrey S.

doi:10.1109/access.2020.2991187

Cited by 60 publications

(61 citation statements)

References 59 publications

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“…Furthermore, the recent studies also pay attention to detecting the precise boundary in the midst of the complex image data. Xie et al [16] utilize the hyperparameters to train and used transfer learning to reduce the training time of the GlacierNet CNN modified from the SegNet [17]. In [18], the deep fully convolutional network dilated kernel (FCN-DK) based on the supervised pixel-wise image classification for improving cadastral boundary detection in urban and semiurban areas is proposed.…”

Section: Introductionmentioning

confidence: 99%

Weighted Mask R‐CNN for Improving Adjacent Boundary Segmentation

Suh

Park

et al. 2021

Journal of Sensors

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In the recent era of AI, instance segmentation has significantly advanced boundary and object detection especially in diverse fields (e.g., biological and environmental research). Despite its progress, edge detection amid adjacent objects (e.g., organism cells) still remains intractable. This is because homogeneous and heterogeneous objects are prone to being mingled in a single image. To cope with this challenge, we propose the weighted Mask R-CNN designed to effectively separate overlapped objects in virtue of extra weights to adjacent boundaries. For numerical study, a range of experiments are performed with applications to simulated data and real data (e.g., Microcystis, one of the most common algae genera and cell membrane images). It is noticeable that the weighted Mask R-CNN outperforms the standard Mask R-CNN, given that the analytic experiments show on average 92.5% of precision and 96.4% of recall in algae data and 94.5% of precision and 98.6% of recall in cell membrane data. Consequently, we found that a majority of sample boundaries in real and simulated data are precisely segmented in the midst of object mixtures.

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Section: Introductionmentioning

confidence: 99%

Weighted Mask R‐CNN for Improving Adjacent Boundary Segmentation

Suh

Park

et al. 2021

Journal of Sensors

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“…With the advances in artificial intelligence, machine learning schemes are now being introduced in glacier mapping. Inspired by the semantic segmentation, Xie et al [133] proposed a convolutional neural network (CNN)-based semantic segmentation framework to extract debris-covered areas. Landsat-8 images, geomorphometric parameters, and DEMs were selected as the input datasets.…”

Section: Remote Sensing Of Glacier Surfacementioning

confidence: 99%

Modeling Surface Processes on Debris-Covered Glaciers: A Review with Reference to the High Mountain Asia

Huo

Chi

2021

Water

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Surface processes on debris-covered glaciers are governed by a variety of controlling factors including climate, debris load, water bodies, and topography. Currently, we have not achieved a general consensus on the role of supraglacial processes in regulating climate–glacier sensitivity in High Mountain Asia, which is mainly due to a lack of an integrated understanding of glacier surface dynamics as a function of debris properties, mass movement, and ponding. Therefore, further investigations on supraglacial processes is needed in order to provide more accurate assessments of the hydrological cycle, water resources, and natural hazards in the region. Given the scarcity of long-term in situ data and the difficulty of conducting fieldwork on these glaciers, many numerical models have been developed by recent studies. This review summarizes our current knowledge of surface processes on debris-covered glaciers with an emphasis on the related modeling efforts. We present an integrated view on how numerical modeling provide insights into glacier surface ablation, supraglacial debris transport, morphological variation, pond dynamics, and ice-cliff evolution. We also highlight the remote sensing approaches that facilitate modeling, and discuss the limitations of existing models regarding their capabilities to address coupled processes on debris-covered glaciers and suggest research directions.

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“…Luus et al, 2015), and automated detection of geological features on Mars (Palafox et al, 2017). In glaciology, CNNs have achieved success in mapping debris-covered land-terminating glaciers (Xie et al, 2020), rock glaciers (Robson et al, 2020), supraglacial lakes (Yuan et al, 2020) and snow cover (Nijhawan et al, 2019). The application of deep learning models in workflows for automated delineation of marine-terminating glacier termini has also been effective, resulting in accuracy comparable to conventional manual methods (Baumhoer et al, 2019;Mohajerani et al, 2019;Zhang et al, 2019).…”

Section: Deep Learning and Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Image Classification of Marine-Terminating Outlet Glaciers using Deep Learning Methods

Marochov

Stokes

Carbonneau

2020

Preprint

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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. Despite the burgeoning availability of medium resolution satellite data, 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 termini. However, at this stage, there remains a paucity of research on the use of deep learning for pixel-level semantic image classification of outlet glacier environments. In this contribution, we apply and test a two-phase deep learning approach based on a well-established convolutional neural network (CNN) called VGG16 for automated classification of Sentinel-2 satellite images. The novel workflow, termed CNN-Supervised Classification (CSC), was originally developed for fluvial settings but is adapted here to produce multi-class outputs for test imagery of glacial environments containing marine-terminating outlet glaciers in eastern Greenland. Results show mean F1 scores up to 95 % for in-sample test imagery and 93 % for out-of-sample test imagery, establishing a state-of-the-art in classification of marine-terminating glacial environments with significant improvements over traditional pixel-based methods such as band ratio techniques. This demonstrates the transferability and robustness of the deep learning workflow for automated classification despite the complex and seasonally variable characteristics of the imagery. Future research could focus on the integration of deep learning classification workflows with platforms such as Google Earth Engine, to more 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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GlacierNet: A Deep-Learning Approach for Debris-Covered Glacier Mapping

Cited by 60 publications

References 59 publications

Weighted Mask R‐CNN for Improving Adjacent Boundary Segmentation

Weighted Mask R‐CNN for Improving Adjacent Boundary Segmentation

Modeling Surface Processes on Debris-Covered Glaciers: A Review with Reference to the High Mountain Asia

Image Classification of Marine-Terminating Outlet Glaciers using Deep Learning Methods

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