Artificial Intelligence and Deep Learning Symposium: Machine Learning for Trajectory Data Mining and Urban Studies

Type: Paper
Theme:
Sponsor Groups: Geographic Information Science and Systems Specialty Group, Cyberinfrastructure Specialty Group, Spatial Analysis and Modeling Specialty Group
Poster #:
Day: 4/13/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Grand Ballroom A, Astor, 2nd Floor
Organizers: Song Gao
Chairs: Song Gao

Description

Recent years have witnessed significant advancements in deep learning, machine learning, as well as many other artificial intelligence (AI) techniques (e.g., ontologies, Linked Data, and knowledge graphs). The new methods and techniques brought by these advancements are transforming geospatial research in a variety of areas. For example, recent studies have shown deep learning techniques coupled with volunteered geographic information (such as OpenStreetMap data) can accurately extract buildings from satellite images for humanitarian mapping. Artificial intelligence methods are also enabling self-driving cars and intelligent transport system by analyzing large amounts of geographic information gathered by traffic cameras and sensors in real time. Many machine learning techniques have facilitated natural language processing, and have helped discover new knowledge from geotagged texts. Advances in knowledge representation and ontology engineering gave birth to novel applications in geographic information retrieval and intelligent search. There also exist many other applications of AI in geospatial research, such as spatial diffusion prediction in epidemiology, urban expansion analysis, and hyperspectral image analysis. In this context, we organize a special symposium focusing on the current status, recent advances, and possible future directions of this exciting research theme at the 2018 AAG Annual meeting, April 10-14, New Orleans, Louisiana. We aim to bring in geographers, spatial modeling experts, computer scientists, spatial data scientists, epidemiologists, urban planners, transportation professionals, and many others to discuss this rapidly developing research frontier. This session mainly focuses on the recent advancement in trajectory data mining and urban structure analysis using machine learning techniques.


Agenda

Type Details Minutes Start Time
Presenter Geonhwa You*, Kyung Hee University, South Korea, Chul Sue Hwang, Kyung Hee University, South Korea, A machine learning approach to urban structure modeling in big data era 20 8:00 AM
Presenter Xiaoyi Zhang*, Zhejiang University, Estimate Spatial Efficiency of Public Bicycle Station using POI data 20 8:20 AM
Presenter CAN WANG*, Tongji University, DE WANG, Tongji University, WEI ZHU, Tongji University, SHAN SONG, Nagoya University, Classification and Prediction of Pedestrian Routes in Urban Commercial Space 20 8:40 AM
Presenter Sean Ahearn*, Hunter College - City University, Somayeh Dodge, Department of Geography, Environment and Society, University of Minnesota, Twin Cities, Minneapolis, MN, USA, Multi-frequency Segmentation and Classification of Trajectories 20 9:00 AM
Presenter Song Gao*, University of Wisconsin, Madison, Kang Liu, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Peiyuan Qiu, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Xiliang Liu, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Bo Yan, UC Santa Barbara, Feng Lu, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Road2Vec: measuring traffic interactions in urban road systems from massive trajectories 20 9:20 AM

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