Koichi Ito
Koichi Ito
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Active Mobility
Translating street view imagery to correct perspectives to enhance bikeability and walkability studies
This study introduces a novel AI-based framework to mitigate perspective biases in vehicle-captured street view imagery, successfully translating car-centric views to pedestrian and cyclist perspectives for more accurate bikeability and walkability assessments in urban planning.
Koichi Ito
,
Matias Quintana
,
Xianjing Han
,
Roger Zimmermann
,
Filip Biljecki
Examining the Causal Impacts of the Built Environment on Cycling Activities Using Time-series Street View Imagery
I attended IATBR 2024 in Vienna, Austria this week and presented my work on the causal impacts of visual elements on cyclist activities!
Jul 15, 2024 12:00 AM
University of Vienna, Austria
Advancing urban modeling with emerging geospatial datasets and AI technologies
I was invited to talk about my research on street view imagery to a group of audience from government agencies and industries
Oct 5, 2023 12:00 AM
Sheraton Towers, Singapore
Sidewalk the Talk: Translating street view imagery to correct perspectives to enhance bikeability and walkability studies
The study develops a new method using deep learning to correct perspective biases in street view imagery, improving the assessment of active transportation infrastructure from the viewpoint of cyclists and pedestrians.
Oct 5, 2023 12:00 AM
Sands Expo and Convention Centre, Level 3
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