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Symposium on Frontiers in CyberGIS and Geospatial Data Science: Spatial Machine Learning and Deep Learning

Type: Paper
Sponsor Groups: Cyberinfrastructure Specialty Group, Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group
Organizers: Jeon-Young Kang, Shaowen Wang


The rapid growth of geospatial data has far exceeded our previous capability of data analytics. In addition to new geospatial computing technologies, GIScientists continuously explore new methods to shift geospatial data analytics towards automatic model building. Recent advancement in deep learning algorithms has proven its success in automatically learning the representative and discriminative features in a hierarchical manner from geospatial big data. For example, high accuracy of land cover classification map has been generated using various convolutional neural network with remotely sensed imagery datasets. However, geospatial data science poses unique challenges in machine learning, such as large-scale network analysis, spatial optimization with scale heterogeneity, multi-temporal modelling, and location inference from large text corpus, to name a few here.


Type Details Minutes Start Time
Presenter Behnam Nikparvar*, University of North Carolina - Charlotte, Jean-Claude Thill, University of North Carolina at Charlotte, A Probabilistic Principal Components Analysis (PPCA) Approach to Impute Missing Values in Spatiotemporal Datasets 15 12:00 AM
Presenter Zewei Xu, University of Illinois at Urbana-Champaign, Nattapon Jaroenchai*, University of Illinois Urbana-Champaign, Shaowen Wang, University of Illinois at Urbana-Champaign, Hydrographic Streamline Detection Using LiDAR and Deep Learning 15 12:00 AM
Presenter Matthew Tenney*, University of Toronto , GIS 9000 | 2019: A GeoSpatial Odyssey 15 12:00 AM
Presenter Zhengcong Yin*, Texas A&M University, Andong Ma, Texas A&M University, Daniel W. Goldberg, Texas A&M University, A Deep Learning Approach for Rooftop Geocoding 15 12:00 AM

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