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GeoAI Symposium: AI for Mapping

Type: Paper
Sponsor Groups:
Organizers: Samantha Arundel, Wenwen Li
Chairs: Samantha Arundel


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 enables 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 a 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 transdisciplinary 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 and high-definition mapping, indoor navigation, historical map digitizing,
gazetteer conflation, geographic feature extraction, geo-ontologies, and place understanding.


Type Details Minutes Start Time
Presenter Sakib Hasan*, University of Wisconsin - Milwaukee, Zengwang Xu, University of Winconsin - Milwaukee, A Review on Deep Learning Applications in Geospatial Social Sciences 15 12:00 AM
Presenter Samantha Arundel*, Center of Excellence for Geospatial Information Science, U.S. Geological Survey, Arthur Chan, Missouri University of Science and Technology, Wenwen Li, Arizona State University, Sizhe Wang, Arizona State University, Chia-Yu Hsu, Arizona State University, TerrainFeatures: a natural feature dataset for machine learning training 15 12:00 AM
Presenter Arthur Chan*, U.S. Geological Survey, Samantha T Arundel , US Geological Survey, Gaurav Sinha, Ohio University, Using custom deep learning to collect spot elevations from US Geological Survey historical topographic maps 15 12:00 AM
Presenter Boleslo E. Romero*, University of New Mexico, Finding anomalies based upon multi-scale spatial dependence 15 12:00 AM

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