GlacierNet2: A hybrid Multi-Model learning architecture for alpine glacier mapping

Xie, Zhiyuan; Haritashya, Umesh K.; Asari, Vijayan K.; Bishop, Michael P.; Kargel, Jeffrey S.; Aspiras, Theus

doi:10.1016/j.jag.2022.102921

Cited by 5 publications

(9 citation statements)

References 68 publications

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“…Moreover, as discussed in our previous study Ref. [60], the differences can also be caused by the nearby non-snow areas merging and small non-snow regions removing processes in the SCAZ estimation module.…”

Section: Resultsmentioning

confidence: 90%

“…[34] and Ref. [60]. Notably, the input raster data were uniformly resampled (using nearest-neighbor interpolation) to the 30-meter resolution instead of the 15-meter resolution used in Ref.…”

Section: Methodology a Datamentioning

confidence: 99%

“…Error in glacier mapping can occur in any region of the glacier. Although GlacierNet2 improves the terminus and ablation estimation over the GlacierNet [60], there still is a space to enhance it by including different mapping strategies, data, network structure, and post-processing algorithms. The network structure can be enhanced by experimenting with multiple structures and optimizing the structure parameters to improve the overall mapping performance, especially in estimating the terminus and ablation zone.…”

Section: B Issues and Improvementsmentioning

confidence: 99%

“…[34] and Ref. [60], so this section mainly focuses on the specific issues in the real-world largescale glacier mapping application.…”

Section: B Issues and Improvementsmentioning

confidence: 99%

“…Multiple CNN models have been applied and evaluated for glacier mapping [34], containing the GlacierNet [33], R2UNet [55], FCDenseNet [56], [57], Res-UNet [41], [46], Mobile-UNet [46], [58], and DeepLabV3+ [59]. According to our previous evaluation [34], the DeepLabV3+ and GlacierNet provide relatively superior performance and, therefore, were modified and utilized to construct the second generation of GlacierNet approach -GlacierNet2 [60]. The GlacierNet2 is a hybrid multi-model learning approach that incorporates CNNs, machine-learning classifier, basinlevel hydrological flow estimation, possessing the ability to regionally map the complete glacier, including ablation and accumulation zones.…”

Section: Introductionmentioning

confidence: 99%

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A Progressive Learning Strategy for Large-Scale Glacier Mapping

2022

Self Cite

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In recent years, the worldwide temperature increase has resulted in rapid deglaciation and a higher risk of glacier-related natural hazards such as flooding and debris flow. Due to the severity of these hazards, continuous observation and detailed analysis of glacier fluctuations are crucial. Many such analyses require an accurately delineated glacier boundary. However, the complexity and heterogeneity of glaciers, particularly debris-covered glaciers (DCGs), poses a challenge for glacier mapping when using conventional remote sensing or machine-learning techniques. Some examples exist about small-scale automated glacier mapping, but large or regional-scale mapping is challenging. Previously, a deep-learning-based approach named GlacierNet2 had been developed to accurately delineate the complete DCG outlines on the regional scope via taking advantage of multiple models. This paper uses a modified version of GlacierNet2 to study the feasibility and effectiveness of large-scale glacier mapping in Nepal Himalaya, Karakoram, and parts of western Himalaya. Also, we propose a large-scale mapping strategy to progressively enhance the network familiarity to varied types of glaciers via systematically repeating the training process. This strategy allows the network to delineate a large number of glaciers while only requiring a small proportion of initial training data. Thus, resulting in a significant drop in labor and expert intervention, which are required for selecting and labeling the training data. Our results show a successful and accurate generation of glacier boundaries with an intersection over union (IOU) score of 0.8115 in the Karakoram and parts of western Himalaya and an IOU of 0.7525 in the Nepal Himalaya. Our work outlines how future efforts of large and global scale mapping can be developed to monitor and analyze glacier dynamics.

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Section: Resultsmentioning

confidence: 90%

Section: Methodology a Datamentioning

confidence: 99%

Section: B Issues and Improvementsmentioning

confidence: 99%

“…[34] and Ref. [60], so this section mainly focuses on the specific issues in the real-world largescale glacier mapping application.…”

Section: B Issues and Improvementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Progressive Learning Strategy for Large-Scale Glacier Mapping

2022

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An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers

Thomas

Robson

Racoviteanu

2023

Front. Remote Sens.

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Evaluating glacial change and the subsequent water stores in high mountains is becoming increasingly necessary, and in order to do this, models need reliable and consistent glacier data. These often come from global inventories, usually constructed from multi-temporal satellite imagery. However, there are limitations to these datasets. While clean ice can be mapped relatively easily using spectral band ratios, mapping debris-covered ice is more difficult due to the spectral similarity of supraglacial debris to the surrounding terrain. Therefore, analysts often employ manual delineation, a time-consuming and subjective approach to map debris-covered ice extents. Given the increasing prevalence of supraglacial debris in high mountain regions, such as High Mountain Asia, a systematic, objective approach is needed. The current study presents an approach for mapping debris-covered glaciers that integrates a convolutional neural network and object-based image analysis into one seamless classification workflow, applied to freely available and globally applicable Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 interferometric coherence, and geomorphometric datasets. The approach is applied to three different domains in the Central Himalayan and the Karakoram ranges of High Mountain Asia that exhibit varying climatic regimes, topographies and debris-covered glacier characteristics. We evaluate the performance of the approach by comparison with a manually delineated glacier inventory, achieving F-score classification accuracies of 89.2%–93.7%. We also tested the performance of this approach on declassified panchromatic 1970 Corona KH-4B satellite imagery in the Manaslu region of Nepal, yielding accuracies of up to 88.4%. We find our approach to be robust, transferable to other regions, and accurate over regional (>4,000 km2) scales. Integrating object-based image analysis with deep-learning within a single workflow overcomes shortcomings associated with convolutional neural network classifications and permits a more flexible and robust approach for mapping debris-covered glaciers. The novel automated processing of panchromatic historical imagery, such as Corona KH-4B, opens the possibility of exploiting a wealth of multi-temporal data to understand past glacier changes.

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Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto

Oktar

Bakırman

Vassilev

et al. 2023

Drones

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Antarctica plays a key role in the hydrological cycle of the Earth’s climate system, with an ice sheet that is the largest block of ice that reserves Earth’s 90% of total ice volume and 70% of fresh water. Furthermore, the sustainability of the region is an important concern due to the challenges posed by melting glaciers that preserve the Earth’s heat balance by interacting with the Southern Ocean. Therefore, the monitoring of glaciers based on advanced deep learning approaches offers vital outcomes that are of great importance in revealing the effects of global warming. In this study, recent deep learning approaches were investigated in terms of their accuracy for the segmentation of glacier landforms in the Antarctic Peninsula. For this purpose, high-resolution orthophotos were generated based on UAV photogrammetry within the Sixth Turkish Antarctic Expedition in 2022. Segformer, DeepLabv3+ and K-Net deep learning methods were comparatively analyzed in terms of their accuracy. The results showed that K-Net provided efficient results with 99.62% accuracy, 99.58% intersection over union, 99.82% precision, 99.76% recall and 99.79% F1-score. Visual inspections also revealed that K-Net was able to preserve the fine details around the edges of the glaciers. Our proposed deep-learning-based method provides an accurate and sustainable solution for automatic glacier segmentation and monitoring.

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GlacierNet2: A hybrid Multi-Model learning architecture for alpine glacier mapping

Cited by 5 publications

References 68 publications

A Progressive Learning Strategy for Large-Scale Glacier Mapping

A Progressive Learning Strategy for Large-Scale Glacier Mapping

An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers

Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto

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