A Deep Learning-Based Geostatistical Framework for Geospatial Data Analysis and Modeling

Authors: Guofeng Cao*, Texas Tech University
Topics: Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: Geostatistics, deep learning, spatial uncertainty, data mining
Session Type: Paper
Day: 4/5/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Roosevelt 3, Marriott, Exhibition Level
Presentation File: No File Uploaded

In the past several years, major advances have been made in the fields of
machine learning and geostatistics. Particularly in machine learning, the
deep neural network-based methods have dramatically improved the
state-of-art in pattern recognition and applications. With a deep neural
network with multiple levels of processing layers, deep learning-based
methods have been shown to excel at discovering intricate high-level of
patterns from high-dimensional data. Recent studies have demonstrated the
initial success of the deep neural network in modeling and analyzing
geospatial data, particularly in remote sensing imagery analysis and
understanding. Few has been done to exploit the power of the deep learning
for general geospatial data analysis and modeling. Geostatistics represents
a conventional approach to characterize and model geospatial variations and
patterns. In this paper, we explored approaches to integrate the
geostatistics and deep learning to take advantages of both sides, and
presented a deep learning-based geostatistical framework for statistical
analysis and modeling of geospatial data. The performances of the presented
framework are demonstrated with real and synthetic cases.

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