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.
|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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