AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: GeoAI - Intelligent Geospatial Analytics
Type: Virtual Paper
Day: 3/1/2022
Start Time: 8:00 AM
End Time: 9:20 AM
Theme:
Sponsor Group(s):
Spatial Analysis and Modeling Specialty Group
, Geographic Information Science and Systems Specialty Group
, Cyberinfrastructure Specialty Group
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Organizer(s):
Di Zhu
, Song Gao
, Eric Shook
, Xiao Huang
Chairs(s):
Di Zhu, University of Minnesota, Twin Cities
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Description:
Geospatial artificial intelligence (GeoAI) has drawn great attention in the inter-discipline of computer science and GIScience. Recently there are many applications in the emerging field of GeoAI that utilize deep learning frameworks (e.g. CNN, GCN, LSTM, GAN, Transformer) for geospatial studies, but not so many works are here to investigate and discuss the intuitive link between critical concepts in traditional spatial analytics (e.g. spatial dependence, scale, distance decay, spatial heterogeneity) and deep learning/machine learning principles (e.g. convolution, embedding, attention). Therefore, it is still blurred to see why AI models could facilitate spatial analysis theoretically and empirically.
This session aims to explore how spatial analytical methods can be enriched with more possibilities when combined with state-of-the-art machine learning/deep learning insights, thus boosting the advancement of intelligent geospatial applications and enlightening future research at the front of GeoAI.
Presentation(s), if applicable
Bo Zhao, ; Deepfake geography? When geospatial data encounter Artificial Intelligence |
Guofeng Cao, University of Colorado - Boulder; A deep learning-based geostatistical framework for geospatial data analysis and modeling |
Armita Kar, ; Inclusive Accessibility – A deep learning-based space-time framework integrating person-based hard and soft constraints |
Xiao Huang, Emory University; Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases |
Chanwoo Jin, Northwest Missouri State University; Revisiting gravity model through explainable GeoAI to predict survivability of restaurants |
Non-Presenting Participants Agenda
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AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: GeoAI - Intelligent Geospatial Analytics
Description
Virtual Paper
Contact the Primary Organizer
Di Zhu - dizhu@umn.edu
Session Sponsored By
