AAG 2023 Symposium on Harnessing the Geospatial Data Revolution for Sustainability Solutions: Geospatial Artificial Intelligence and Deep Learning
The session recording will be archived on the site until June 25th, 2023
This session was streamed but not recorded
Date: 3/27/2023
Time: 2:40 PM - 4:00 PM
Room: Governors Square 16, Sheraton, Concourse Level
Type: Paper,
Theme:
Curated Track:
Sponsor Group(s):
Cyberinfrastructure Specialty Group, Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group
Organizer(s):
Nattapon Jaroenchai University of Illinois Urbana-Champaign
Alexander Michels University of Illinois Urbana-Champaign
Jinwoo Park University of Illinois Urbana-Champaign
Shaowen Wang University of Illinois Urbana-Champaign
Chair(s):
Nattapon Jaroenchai University of Illinois 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.
Symposium Description:
The Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE, https://iguide.illinois.edu) is supported by the National Science Foundation (NSF) as part of its Harnessing the Data Revolution Big Idea initiative (https://www.nsf.gov/news/special_reports/big_ideas/harnessing.jsp). Sponsored by I-GUIDE, this symposium will explore theories, concepts, methods, and tools focused on data-intensive geospatial understanding for driving innovative artificial intelligence (AI) and cyberGIS (cyber-based geographic information science and systems) approaches to address sustainability challenges such as aging infrastructure, biodiversity loss, and food and water insecurity.
At the AAG 2023 annual meeting, the Symposium on Harnessing the Geospatial Data Revolution for Sustainability Solutions will be held by building on the successes of previous Symposia focused on cyberGIS and geospatial data science at AAG annual meetings since 2011. A suite of paper and panel sessions will address cutting-edge advances of cyberGIS, geospatial AI and data science, and fundamental geospatial understanding derived from spatial and spatiotemporal data synthesis. The topical themes of the symposium will include, but are not limited to, frontiers of cyberGIS, geospatial AI and data science, high-performance computing approaches to geographic problem solving, geographic approaches to resilience and sustainability challenges enabled by AI and cyberGIS, and challenges and opportunities of education and workforce development in harnessing the geospatial data revolution.
Presentations (if applicable) and Session Agenda:
Seung Bae Jeon, Chosun University |
Short-term Traffic Speed Prediction of Urban Road Networks using the Integration of Spatio-Temporal Graph Convolutional Networks and Convolutional Neural Networks |
Nattapon Jaroenchai, University of Illinois Urbana-Champaign |
Transfer Learning with Convolutional Neural Networks for Hydrological Streamline Delineation |
Yuhao Wang |
Predict Soybean Yield in Argentina Using MODIS Data Deep Learning Methods |
Mashoukur Rahaman, University of Florida |
Integrating deep learning and machine learning methods to track air pollution amounts and impacts on vegetation cover, using remote sensing data |
Non-Presenting Participants
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AAG 2023 Symposium on Harnessing the Geospatial Data Revolution for Sustainability Solutions: Geospatial Artificial Intelligence and Deep Learning
Description
Type: Paper,
Date: 3/27/2023
Time: 2:40 PM - 4:00 PM
Room: Governors Square 16, Sheraton, Concourse Level
Contact the Primary Organizer
Nattapon Jaroenchai University of Illinois Urbana-Champaign
nj7@illinois.edu
Session Sponsored By
