Estimation of glacier mass balance using remote sensing and GIS technology in the Hindu Kush region of northern Pakistan

Bibi, Sidra; Shafique, Muhammad; Ali, Neelum; Nazneen, Shahla; Ali, Liaqat; Baig, Siddique Ullah; Gul, Rehman

doi:10.1007/s12145-022-00930-4

Cited by 4 publications

(1 citation statement)

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“…Digital elevation model (DEM) is one of the sources of national geospatial infrastructure (Xiong et al, 2014), playing a crucial role in various research fields such as global climate change (Rousta et al, 2023), ocean tidal movements (Nagaraj and Kumar, 2024), and glacier evolution detection (Bibi et al, 2023). Constructing global ocean DEM represents an advanced branch of marine geology and oceanography, providing essential information for understanding seafloor tectonic movements and seafloor evolution processes.…”

Section: Introductionmentioning

confidence: 99%

From Land to Ocean: Bathymetric Terrain Reconstruction via Conditional Generative Adversarial Network

Zhang,

Wen,

Huo

et al. 2024

Preprint

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Acquiring global ocean digital elevation model (DEM) is a forefront branch of marine geology and hydrographic survey that plays a crucial role in the study of the Earth's system and seafloor's structure. Due to limitations in technological capabilities and surveying costs, large-scale sampling of ocean depths is very coarse, making it challenging to directly create complete ocean DEM. Many traditional interpolation and deep learning methods have been applied to reconstruct ocean DEM images. However, the continuity and heterogeneity of ocean terrain data are too complex to be approximated effectively by traditional interpolation models. Meanwhile, due to the scarcity of available data, training an sufficient network directly with deep learning methods is difficult. In this work, we propose a conditional generative adversarial network (CGAN) based on transfer learning, which applies knowledge learned from land terrain to ocean terrain. We pre-train the model using land DEM data and fine-tune it using ocean DEM data. Specifically, we utilize randomly sampled ocean terrain data as network input, employ CGAN with U-Net architecture and residual blocks to capture terrain features of images through adversarial training, resulting in reconstructed bathymetric terrain images. The training process is constrained by the combined loss composed of adversarial loss, reconstruction loss, and perceptual loss. Experimental results demonstrate that our approach reduces the required amount of training data, and achieves better reconstruction accuracy compared to traditional methods.

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Section: Introductionmentioning

confidence: 99%