GeoAI in Mapping

Type: Virtual Paper
Theme:
Sponsor Groups:
Poster #:
Day: 4/10/2021
Start / End Time: 3:05 PM / 4:20 PM (PDT)
Room: Virtual 43
Organizers: Samantha Arundel, Yao-Yi Chiang
Chairs: Samantha Arundel

Call for Submissions

The use of artificial intelligence in the geographic realm (GeoAI) has made great inroads to answering spatial questions in the GIS, cartography and mapping fields. There are many applications that successfully incorporate AI solutions with mapping problems such as geographic feature extraction, geo-ontology design, understanding of place, historical map digitization, vehicle trajectory prediction, indoor navigation, and gazetteer conflation. A fast-growing field of inquiry, this session, presented in 2020 as AI for Mapping, will present current research and describe technologies continuing to be developed today and in the future. We welcome any presenters addressing these and any related subjects, including both basic and applied research on the role of GeoAI in addressing mapping issues.


Description

The use of artificial intelligence in the geographic realm (GeoAI) has made great inroads to answering spatial questions in the GIS, cartography and mapping fields. There are many applications that successfully incorporate AI solutions with mapping problems such as geographic feature extraction, geo-ontology design, understanding of place, historical map digitization, vehicle trajectory prediction, indoor navigation, and gazetteer conflation. A fast-growing field of inquiry, this session, presented in 2020 as AI for Mapping, will present current research and describe technologies continuing to be developed today and in the future. We welcome any presenters addressing these and any related subjects, including both basic and applied research on the role of GeoAI in addressing mapping issues.


Agenda

Type Details Minutes Start Time
Presenter Samantha Arundel*, Center of Excellence for Geospatial Information Science, U.S. Geological Survey, Gaurav Sinha, Ohio University, Dennis Powon, U.S. Geological Survey, Assessing viability of deep learning optical character recognition data and models for detection and interpretation of annotations in topographic maps 15 3:05 PM
Presenter Ji Won Suh*, Department of Geography, University of Connecticut, William Ouimet, Department of Geosciences, Department of Geography, University of Connecticut, Jonathan Leonard, Department of Geography, University of Connecticut, Automated Mapping of Stone Walls in the Northeastern USA using UNet based Deep Learning 15 3:20 PM
Presenter Yogita Karale*, University of Texas At Dallas, May Yuan, University of Texas at Dallas, How does convolution in neural networks improve hourly PM 2.5 estimates 15 3:35 PM
Presenter Victor Gregor Limon*, University of Hawaii at Manoa, Mapping the population density of an island city using a species distribution model 15 3:50 PM
Presenter Zhe Wang*, University of Idaho, chao fan, Assistant Professor, The effectiveness of the U-net on mapping urban tree canopy 15 4:05 PM

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