
The Spatially Constrained Bayesian Network (SCB-Net) is a deep learning approach that generates accurate lithological predictions while quantifying uncertainty, by effectively integrating auxiliary data and sparse field observations.


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spatially-constrained-bayesian-network-scb-net-for-accurate-and-uncertainty-aware-lithological-mapping


Spatially Constrained Bayesian Network (SCB-Net) for Accurate and Uncertainty-Aware Lithological Mapping


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The study compares five roughness descriptors for LiDAR-derived digital elevation models, highlighting the importance of multiple descriptors in characterizing terrain surface roughness.


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Comparing Roughness Descriptors for LiDAR-derived DEMs



The author presents a parallel implementation of the Vecchia approximation technique, utilizing batched matrix computations on contemporary GPUs to speed up evaluating the log-likelihood function.


gpu-accelerated-vecchia-approximations-of-gaussian-processes-for-geospatial-data-using-batched-matrix-computations


GPU-Accelerated Vecchia Approximations of Gaussian Processes for Geospatial Data using Batched Matrix Computations
