GeoAI and Deep Learning Symposium: Machine Learning and Deep Learning for Trajectory Data Analysis

Type: Paper
Theme:
Sponsor Groups: Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group, Cyberinfrastructure Specialty Group
Poster #:
Day: 4/5/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Washington 6, Marriott, Exhibition Level
Organizers: Song Gao, Sean Ahearn, Chaogui Kang
Chairs: Song Gao

Description

In today’s era of big data, advanced algorithms, and immense computational power, artificial intelligence (AI) is bringing tremendous opportunities and challenges to geospatial research. Big data enable computers to observe and learn the world from many angles, while high performance machines support the training development and application deployment of AI models within reasonable amount of time. Recent years have witnessed significant advances in the integration of geography and AI in both academia and industry, and the outcome is an exciting and interdisciplinary area-- GeoAI. There have already been many successful studies. Focusing on modeling the physical nature, a recent publication in PNAS has shown that deep learning can improve the representation of clouds that are smaller than the grid resolutions of climate models. Examining the human society, AI and natural language processing methods, such as word embeddings, are helping quantify changes in stereotypes and attitudes toward women and ethnic minorities over 100 years in the United States. There are also many other applications that effectively integrate AI with problems in geospatial studies, such as vehicle trajectory prediction, indoor navigation, historical map digitizing, gazetteer conflation, geographic feature extraction, geo-ontologies, and place understanding.

Building on the great success of the 1st symposium in AAG 2018, we are organizing the 2nd AAG Symposium on GeoAI and Deep Learning for Geospatial Research focusing on the current status, recent advances, and possible future directions of this exciting research theme at the 2019 AAG Annual meeting, April 3-7, Washington DC. We aim to bring together geographers, GI scientists, spatial modeling experts, computer scientists, spatial data scientists, epidemiologists, urban planners, transportation professionals, and many others to discuss this rapidly developing research frontier. In particular, we hope to provide a venue for researchers from all geospatial disciplines to start the dialog on how to fertilize this exciting field of GeoAI, how can we better prepare our students with essential knowledge and skills, and how can we foster cross-discipline collaborations.

This session " Machine Learning and Deep Learning for Trajectory Data Analysis" will invite presentations about latest progress on trajectory data modeling and analysis.


Agenda

Type Details Minutes Start Time
Presenter Sean Ahearn*, Hunter College - City University, The Trajectory as an Intelligent Agent 20 3:05 PM
Presenter Somayeh Dodge*, University of Minnesota, Jasper Johnson, University of Minnesota, Sean Ahearn, CUNY- Hunter College, Mining interaction patterns in coarse trajectory data sets 20 3:25 PM
Presenter Fan Zhang*, Massachusetts Institute of Technology, Di Zhu, Peking University, Yu Liu, Peking University, Social sensing from street-level imagery: a case study in learning urban mobility patterns 20 3:45 PM
Presenter Peter Lenz*, Dstillery, To measure macro, first think micro: petabyte scale behavioral modeling at Dstillery 15 4:05 PM
Presenter Mingxiao Li, State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Song Gao*, Geospatial Data Science Lab, Department of Geography, University of Wisconsin, Madison, Feng Lu, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Hengcai Zhang, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data 15 4:20 PM
Discussant Chaogui Kang Wuhan University 10 4:35 PM

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