Browsing by Subject "deep learning"
Now showing items 1-7 of 7
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A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland
; Eltaher, Mahmoud; ; Breuß, Michael; Raab, Alexandra; Schneider, Anna (Archaeological Prospection, 2021-02-02)
Due to complicated backgrounds and unclear target orientation, automated object detection is difficult in the field of archaeology. Most of the current convolutional neural network (CNN) object‐oriented detection techniques ... -
Data‐Driven Medium‐Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench
; (Journal of Advances in Modeling Earth Systems, 2021-02-16)
Numerical weather prediction has traditionally been based on the models that discretize the dynamical and physical equations of the atmosphere. Recently, however, the rise of deep learning has created increased interest ... -
Deep Emulators for Differentiation, Forecasting, and Parametrization in Earth Science Simulators
; (Journal of Advances in Modeling Earth Systems, 2021-06-28)
To understand and predict large, complex, and chaotic systems, Earth scientists build simulators from physical laws. Simulators generalize better to new scenarios, require fewer tunable parameters, and are more interpretable ... -
Global Drivers and Transport Mechanisms of Lunar Rockfalls
; ; ; (Journal of Geophysical Research: Planets, 2021-10-18)
The long‐ and short‐term drivers and transport mechanisms of lunar rockfalls are currently not well understood, but could provide valuable information about the geologic processes that still shape the surface of the Moon ... -
Limited angle tomography for transmission X-ray microscopy using deep learning
Huang, Yixing; Wang, Shengxiang; Guan, Yong; (Journal of Synchrotron Radiation, 2020)
In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision with other system parts or high attenuation at certain tilting angles. Image ... -
Reconstruction of the Basin‐Wide Sea‐Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks
Zhang, Zeguo;; (Journal of Geophysical Research: Oceans, 2020-12-03)
We present an application of generative adversarial networks (GANs) to reconstruct the sea level of the North Sea using a limited amount of data from tidal gauges (TGs). The application of this technique, which learns how ... -
Self‐Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements
; Saynisch‐Wagner, Jan; ; ; Thomas, Maik (Geophysical Research Letters, 2020-08-28)
Quantifying and monitoring terrestrial water storage (TWS) is an essential task for understanding the Earth's hydrosphere cycle, its susceptibility to climate change, and concurrent impacts for ecosystems, agriculture, and ...
