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GlacierNet2: A hybrid Multi-Model learning architecture for alpine glacier mapping
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-07-20 , DOI: 10.1016/j.jag.2022.102921
Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus H. Aspiras

In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed observations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an earlier proposed deep-learning-based approach, GlacierNet, which was developed to exploit a convolutional neural-network segmentation model to accurately outline regional DCG ablation zones. Specifically, we developed an enhanced GlacierNet2 architecture that incorporates multiple models, automatic post-processing, and basin-level hydrological flow techniques to improve the mapping of DCGs such that it includes both the ablation and accumulation zones. Experimental evaluations demonstrate that GlacierNet2 improves the estimation of the ablation zone and allows a high level of intersection over union (IOU: 0.8839) score, which is higher than the GlacierNet (IOU: 0.8599). The proposed architecture provides complete glacier (both accumulation and ablation zone) outlines at regional scales, with an overall IOU score of 0.8619. This is a crucial first step in automating complete glacier mapping that can be used for accurate glacier modeling or mass-balance analysis.



中文翻译:

GlacierNet2:用于高山冰川映射的混合多模型学习架构

近几十年来,气候变化 显着 影响冰川动态,导致质量损失和冰川相关灾害的风险增加,包括冰川上和冰川前湖的发展,以及灾难性的爆发洪水。快速变化的条件要求对气候冰川动态进行连续和详细的观察和分析。关于冰川几何的专题和定量信息对于了解气候强迫和冰川对气候变化的敏感性至关重要,然而,基于使用光谱信息和传统的机器学习技术,准确地绘制碎片覆盖冰川 (DCG) 的地图是出了名的困难。本研究的目的是改进较早提出的基于深度学习的方法 GlacierNet, 它是为了利用卷积神经网络分割模型来准确勾勒区域 DCG 消融区域而开发的。具体来说,我们开发了一个增强的 GlacierNet2 架构,该架构 结合了多个模型、自动后处理和流域级水文流动技术,以改进 DCG 的映射,使其包括消融区和聚集区。实验评估表明 GlacierNet2 改进了消融区域的估计并允许 高水平的并集交集(IOU:0.8839)得分,高于 GlacierNet(IOU:0.8599)。所提出的架构在区域尺度上提供了完整的冰川(积累区和消融区)轮廓,总体 IOU 得分为 0.8619。这是自动化完整冰川绘图的关键第一步,可用于准确的冰川建模或质量平衡分析。

更新日期:2022-07-20
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