We developed ZenSVI, a free and open-source Python package that integrates and implements the entire process of street view imagery analysis, supporting a wide range of use cases.

This study used historical street view imagery and cyclist data from London to establish causal relationships between urban features (like vegetation, slope, and bike lanes) and cycling patterns, finding that their impacts vary by population density and providing actionable insights for urban planning.

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.

This study reviewed 393 papers on how visual characteristics of the built environment influence urban perception, highlighting trends, limitations, and future research opportunities, and demonstrated the efficacy of using machine learning to semi-automate the review process.

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.

We advanced the comprehensive assessment of bikeability using street view imagery and computer vision.

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.