Koichi Ito
Koichi Ito
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Street View Imagery
Assessing the Equity and Evolution of Urban Visual Perceptual Quality with Time Series Street View Imagery
This study introduces a cost-effective, deep learning-based method using time-series street view imagery to evaluate and monitor neighborhood environmental quality, offering a scalable tool for improving urban policy and design.
Zeyu Wang
,
Koichi Ito
,
Filip Biljecki
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
Assessing bikeability with street view imagery and computer vision
Can we improve the scalability of bikeability assessment with street view imagery?
Koichi Ito
Street view imagery in urban analytics and GIS: A review
Street view imagery is booming. Check how to use it for your research.
Koichi Ito
Assessing bikeability with street view imagery and computer vision
We advanced the comprehensive assessment of bikeability using street view imagery and computer vision.
Koichi Ito
,
Filip Biljecki
Street view imagery in urban analytics and GIS: A review
We have provided an extensive review of the use of street-level imagery in urban studies and mapping, through the examination of 250 recently published papers. There are three takeaways we highlight to conclude the paper, which we believe is the most comprehensive one detailing the diverse role of street view imagery in the context of urban analytics and GIS.
Filip Biljecki
,
Koichi Ito
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