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Annals of the American Association of Geographers Volume 114, 2024 - Issue 5
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Articles

Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery

Fan Zhanga Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-3643-018XView further author information
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Arianna Salazar-Mirandab Senseable City Lab, Massachusetts Institute of Technology, USA;c Department of Urban Studies and Planning, Massachusetts Institute of Technology, USAORCID Iconhttps://orcid.org/0000-0003-4093-9250View further author information
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Fábio Duarteb Senseable City Lab, Massachusetts Institute of Technology, USAORCID Iconhttps://orcid.org/0000-0003-0909-5379View further author information
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Lawrence Valec Department of Urban Studies and Planning, Massachusetts Institute of Technology, USAView further author information
,
Gary Hackc Department of Urban Studies and Planning, Massachusetts Institute of Technology, USAView further author information
,
Min Chend Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, ChinaORCID Iconhttps://orcid.org/0000-0001-8922-8789View further author information
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Yu Liua Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, ChinaORCID Iconhttps://orcid.org/0000-0002-0016-2902View further author information
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Michael Battye Centre for Advanced Spatial Analysis, Faculty of the Built Environment, University College London, UKORCID Iconhttps://orcid.org/0000-0002-9931-1305View further author information
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Carlo Rattib Senseable City Lab, Massachusetts Institute of Technology, USA;c Department of Urban Studies and Planning, Massachusetts Institute of Technology, USAView further author information
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Pages 876-897 | Received 12 Jul 2021, Accepted 12 Dec 2023, Published online: 08 Apr 2024

Figures & data

Figure 1. Framework of urban visual intelligence. The framework outlines key aspects related to the urban physical environment. It focuses on using visual intelligence technologies to observe, measure, and represent physical environments and explore their interactions with socioeconomic dimensions at various levels and scales.

Figure 1. Framework of urban visual intelligence. The framework outlines key aspects related to the urban physical environment. It focuses on using visual intelligence technologies to observe, measure, and represent physical environments and explore their interactions with socioeconomic dimensions at various levels and scales.

Figure 2. Sources of street-level imagery.

Figure 2. Sources of street-level imagery.

Figure 3. Three typical computer vision tasks in urban applications: (A) scene classification, (B) object detection, and (C) semantic segmentation.

Figure 3. Three typical computer vision tasks in urban applications: (A) scene classification, (B) object detection, and (C) semantic segmentation.
 

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