GeoAI and Deep Learning Symposium: Urban Visual Intelligence
The session recording will be archived on the site until June 25th, 2023
This session was streamed but not recorded
Date: 3/26/2023
Time: 10:20 AM - 11:40 AM
Room: Capitol Ballroom 3, Hyatt Regency, Fourth Floor
Type: Paper,
Theme:
Curated Track:
Sponsor Group(s):
Cyberinfrastructure Specialty Group, Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group
Organizer(s):
Yuhao Kang University of Wisconsin-Madison
Fabio Duarte MIT Senseable City Lab
Filip Biljecki National University of Singapore
Fan Zhang Hong Kong University of Science and Technology
Chair(s):
Yuhao Kang University of Wisconsin-Madison
Fabio Duarte MIT Senseable City Lab
Description:
Images have been central to how urbanists perceive and understand cities. They have shaped the way cities are designed and have informed the set of modern tools and methodologies used by urban planners to measure the physical environment and its quality. In the era of geospatial data science, a massive amount of image sources including remote sensing imagery, street view imagery as well as geo-tagged social media images provide rich geolocated image data sources for researchers to observe, perceive, and understand the built environment. Benefiting from the advances in deep learning and computer vision techniques, high-level semantic information can be now extracted from images automatically and efficiently. Such diverse sources of geolocated imagery have been widely used in multiple fields, such as urban planning and design, autonomous vehicles, digital twins, city information modeling, public health, environmental criminology, tourism, real estate, and energy consumption. It provides a new perspective to observe human settlement and to further understand the patterns of human-environment interactions.
Presentations (if applicable) and Session Agenda:
Hyunseo Park |
Automated quantification of greenspace maintenance level assessment using Street View images and deep learning methods |
Jiyoung Lee, University Of Nebraska - Lincoln |
Understanding Street Theft Hotspots Using Machine Learning and Google Street Image |
Filip Biljecki |
Crowdsourced street view imagery and urban informatics |
Kee Moon Jang, Massachusetts Institute of Technology |
Discovering inequality in green space exposure from street view images: a case study in 25 small- and medium-sized cities in the U.S. |
Challenges and Future of Urban Visual Intellience |
Non-Presenting Participants
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GeoAI and Deep Learning Symposium: Urban Visual Intelligence
Description
Type: Paper,
Date: 3/26/2023
Time: 10:20 AM - 11:40 AM
Room: Capitol Ballroom 3, Hyatt Regency, Fourth Floor
Contact the Primary Organizer
Yuhao Kang University of Wisconsin-Madison
yuhao.kang@wisc.edu
Session Sponsored By
