AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: Geospatial Artificial Intelligence and Deep Learning
Type: Virtual Paper
Day: 2/26/2022
Start Time: 2:00 PM
End Time: 3:20 PM
Theme:
Sponsor Group(s):
Cyberinfrastructure Specialty Group
, Geographic Information Science and Systems Specialty Group
, Spatial Analysis and Modeling Specialty Group
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Organizer(s):
Nattapon Jaroenchai
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Chairs(s):
Nattapon Jaroenchai, University of Illinois at Urbana-Champaign
; Shaowen Wang, University of Illinois at Urbana-Champaign
Description:
Recent advancements in high-performance computing and hardware have resulted in several state-of-the-art machine learning approaches (e.g., decision tree learning, reinforcement learning, inductive logic programming, Bayesian networks, and clustering) that can be applied to geospatial analyses. Deep learning has become the hottest trend in geospatial applications, especially in the case of supervised deep convolutional neural networks, which have attracted great interest in the computer vision and image processing communities.
These advancements are driven by the increasing need to process large amounts of data generated by the ever-increasing availability of sensors in remote sensing. This unprecedented amount of data generated every day requires the use of AI for exploration and knowledge extraction.
Many AI algorithms, however, are still in their immaturity in terms of scientific understanding. For example, CNNs are often constructed through trial and error. Basic questions such as "How many layers should be utilized in total?" remain. While researchers have access to a vast library of diverse AI algorithms, AI must be employed in conjunction with physical principles and scientific interpretation. Therefore, this session focuses on further understanding the method for applying AI and deep learning in geospatial research such as algorithm development, data training strategies, and implementations.
Presentation(s), if applicable
Nattapon Jaroenchai, Geography, University Of Illinois, Urbana Champain; Transfer Learning with a Convolutional Neural Network for Hydrological Streamline Detection |
Tianyang Chen, University of North Carolina - Charlotte; Empirical knowledge related to deep learning-driven 3D point cloud classification in 3D GIS |
Larry Stanislawski, USGS Center for Excellence in Geospatial Sciences; Assessment of vector hydrography derived from deep learning of remotely sensed data |
Non-Presenting Participants Agenda
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AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: Geospatial Artificial Intelligence and Deep Learning
Description
Virtual Paper
Contact the Primary Organizer
Nattapon Jaroenchai - nj7@illinois.edu