Author: jinmeng

Sessions at the 2023 AAG GeoAI Symposium

jinmeng

Sessions at the 2023 AAG GeoAI Symposium (PDF download)

Symposium Lead Organizers:

Yingjie Hu, University at Buffalo

Song Gao, University of Wisconsin, Madison

Wenwen Li, Arizona State University

Dalton Lunga, Oak Ridge National Laboratory

Orhun Aydin, Saint Louis University

Shawn Newsam, University of California, Merced

3/23/2023, Thursday

GeoAI and Deep Learning Symposium: GeoAI for Feature Detection and Recognition 

GeoAI and Deep Learning Symposium: Deploying AI for Geospatial Data and Remote Sensing: Advances, Challenges and Obstacles 

GeoAI and Deep Learning Symposium: A 5-year Milestone: Advances and Limitations in GeoAI Research So Far 

  • Session Link: https://aag.secure-platform.com/aag2023/solicitations/39/sessiongallery/5663
  • Organizers: Yingjie Hu, Song Gao, Wenwen Li, Dalton Lunga, Orhun Aydin, and Shawn Newsame
  • Panelists: Michael Goodchild, University of California Santa Barbara, A-Xing Zhu, University of Wisconsin, Madison, May Yuan, University of Texas Dallas, Orhun Aydin, Saint Louis University, Budhendra Bhaduri, Oak Ridge National Laboratory)
  • Date: 3/23/2023 (Thursday)
  • Time: 12:50 PM – 2:10 PM Mountain Time
  • Room: Capitol Ballroom 1, Hyatt Regency, Fourth Floor

GeoAI and Deep Learning Symposium: Emerging Geo-Data Applications in Human Mobility Analysis 

GeoAI and Deep Learning Symposium: GeoAI for Cartography and Mapping 

3/24/2023, Friday

GeoAI and Deep Learning Symposium: Spatial Data Science for Ecosystem Conservation and Biodiversity

GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence I

GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence II

GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence III

3/25/2023, Saturday

GeoAI and Deep Learning Symposium: Intelligent Geospatial Analytics 

GeoAI and Deep Learning Symposium: GeoAI for Disaster Resilience 

3/26/2023, Sunday

GeoAI and Deep Learning Symposium: Urban Visual Intelligence 

GeoAI and Deep Learning Symposium: Geoprivacy and Ethics in Geospatial Data and GeoAI 

Program Committee:

Sean C. Ahearn, Hunter College — CUNY 

Samantha T. Arundel, US Geological Survey

Orhun Aydin, Saint Louis University

Andrea Ballatore, King’s College London

Budhendra Bhaduri, Oak Ridge National Lab

Ling Bian, University at Buffalo

Kai Cao, East China Normal University

Guofeng Cao, University of Colorado, Boulder

Yao-Yi Chiang, University of Minnesota-Twin Cities

Somayeh Dodge, University of California Santa Barbara

Chen-Chieh Feng, National University of Singapore

Amy Frazier, Arizona State University

Michael F. Goodchild, University of California, Santa Barbara

Majid Hojati, Wilfrid Laurier University

Yingjie Hu, University at Buffalo

Xiao Huang, University of Arkansas

Qunying Huang, University of Wisconsin, Madison

Jamon Van Den Hoek, Oregon State University 

Krzysztof Janowicz, University of Vienna & University of California, Santa Barbara

Chaogui Kang, China University of Geosciences

Yuhao Kang, University of Wisconsin, Madison

Carsten Keßler, Aalborg University Copenhagen

Morteza Karimzadeh, University of Colorado Boulder

Jina Kim, University of Minnesota

Junghwan Kim, Virginia Tech

Nina Lam, Louisiana State University

Xiaojiang Li, Temple University

Wenwen Li, Arizona State University

Xiao Li, University of Oxford

Yu Liu, Peking University, China

Tao Liu, Michigan Technological University

Grant McKenzie, McGill University

Antonio Medrano, Texas A&M University Corpus Christi

Gengchen Mai, University of Georgia

Yi Qiang, University of South Florida

Alex Sorokine, Oak Ridge National Lab

Avipsa Roy, University of California, Irvine

Kathleen Stewart, University of Maryland, College Park

Marcela Suárez, Penn State University

Wenwu Tang, University of North Carolina at Charlotte

Daoqin Tong, Arizona State University

Ming-Hsiang Tsou, San Diego State University

Mingshu Wang, University of Glasgow

Shaohua Wang, Chinese Academy of Sciences

Shaowen Wang, University of Illinois, Urbana-Champaign

Zhangyu Wang, University of California Santa Barbara

Dawn Wright, Esri Inc.

Hsiuhan (Lexie) Yang, Oak Ridge National Laboratory

Haowen Xu, Oak Ridge National Laboratory

Angela Yao, University of Georgia

Xinyue Ye, Texas A&M University

Eun-Hye Enki Yoo, University at Buffalo

Manzhu Yu, Penn State University

May Yuan, University of Texas at Dallas

Fan Zhang, Hong Kong University of Science and Technology

Hongyu Zhang, McGill University

Bo Zhao, University of Washington

A-Xing Zhu, University of Wisconsin, Madison

Di Zhu, University of Minnesota, Twin Cities

Lei Zou, Texas A&M University

This symposium is sponsored by: AAG GISS, CI and SAM specialty groups

New research paper on privacy-preserved location analysis using federated learning

jinmeng

Rao, J., Gao, S.*, Li, M., & Huang, Q. (2021). A privacy‐preserving framework for location recommendation using decentralized collaborative machine learning. Transactions in GIS. 25(3), 1153-1175.

Abstract: The nowadays ubiquitous location-aware mobile devices have contributed to the rapid growth of individual-level location data. Such data are usually collected by location-based service platforms as training data to improve their predictive models’ performance, but the collection of such data may raise public concerns about privacy issues. In this study, we introduce a privacy-preserving location recommendation framework based on a decentralized collaborative machine learning approach: federated learning. Compared with traditional centralized learning frameworks, we keep users’ data on their own devices and train the model locally so that their data remain private. The local model parameters are aggregated and updated through secure multiple-party computation to achieve collaborative learning among users while preserving privacy. Our framework also integrates information about transportation infrastructure, place safety, and flow-based spatial interaction to further improve recommendation accuracy. We further design two attack cases to examine the privacy protection effectiveness and robustness of the framework. The results show that our framework achieves a better balance on the privacy–utility trade-off compared with traditional centralized learning methods. The results and ensuing discussion offer new insights into privacy-preserving geospatial artificial intelligence and promote geoprivacy in location-based services.

ACKNOWLEDGMENT: We acknowledge the funding support provided by the American Family Insurance Data Science Institute Funding Initiative at the University of Wisconsin-Madison. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funder.

New Research Paper on Trajectory Privacy Protection accepted in GIScience 2021

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Reference: Rao, J., Gao, S., Kang, Y., & Huang, Q. (2020). LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection. In the Proceedings of the 11th International Conference on Geographic Information Science (GIScience 2021), No. 12; pp. 12:1–12:17. DOI: 10.4230/LIPIcs.GIScience.2021.12 [PDF]

Abstract: The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection.