Deep Learning Techniques to Evaluate Changes in Walkability in Downtown Orlando via Street Panorama Imagery

This study examines changes in walkability in downtown Orlando from 2018 to 2021 using Google Street View data and deep learning techniques. The results showed significant enhancements in greenery, increasing from 7.2% to 11.9%, and sidewalk pavement coverage, rising from 10.5% to 15.8%. However, outdoor openness decreased from 21.3% to 18.7% due to new high-rise buildings. Variations were evident between the east and west sides of downtown, divided by Interstate-4, with the east side having higher sidewalk pavement coverage and greenery.

Authors

Publication Details

Journal
Findings
Published
2024-09-26
DOI
https://doi.org/10.32866/001c.123767
Citations
2
Primary Topic
Urban Transport and Accessibility
Type
article
Field-Weighted Citation Impact
1.37
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article

Deep Learning Techniques to Evaluate Changes in Walkability in Downtown Orlando via Street Panorama Imagery

Chia-Yuan Yu
2 citations
Findings
Urban Transport and Accessibility
1.37
article

Deep Learning Techniques to Evaluate Changes in Walkability in Downtown Orlando via Street Panorama Imagery

Chia-Yuan Yu
article en
2 citations

Abstract

This study examines changes in walkability in downtown Orlando from 2018 to 2021 using Google Street View data and deep learning techniques. The results showed significant enhancements in greenery, increasing from 7.2% to 11.9%, and sidewalk pavement coverage, rising from 10.5% to 15.8%. However, outdoor openness decreased from 21.3% to 18.7% due to new high-rise buildings. Variations were evident between the east and west sides of downtown, divided by Interstate-4, with the east side having higher sidewalk pavement coverage and greenery.

Findings
Openalex Percentile: Top 16%
Urban Transport and Accessibility
1.37
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Deep Learning Techniques to Evaluate Changes in Walkability in Downtown Orlando via Street Panorama Imagery — Chia-Yuan Yu · Findings (2024) | TGRS Research Map | TGRS