To use or not to use proprietary street view images in (health and place) research? That is the question
Not on the map. Why?
Abstract (arXiv)
Computer vision-based analysis of street view imagery has transformative impacts on environmental assessments. Interactive web services, particularly Google Street View, play an ever-important role in making imagery data ubiquitous. Despite the technical ease of harnessing millions of Google Street View images, this article questions the current practices in using this proprietary data source from a European viewpoint. Our concern lies with Google's terms of service, which restrict bulk image downloads and the generation of street view image-based indices. To reconcile the challenge of advancing society through groundbreaking research while maintaining data license agreements and legal integrity, we believe it is crucial to 1) include an author's statement on using proprietary street view data and the directives it entails, 2) negotiate academic-specific license to democratize Google Street View data access, and 3) adhere to open data principles and utilize open image sources for future research.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 0 of its 20 references that are in the map. First posted on arXiv 18 Feb 2024.
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Citing papers per year: 2024: 3; 2025: 6; 2026: 1.
10 papers.
- 2024Coverage and Bias of Street View Imagery in Mapping the Urban Environmentcited by 11
- 2024A citizen science toolkit to collect human perceptions of urban environments using open street view imagescited by 9
- 2025OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagerycited by 8
- 2024ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Sciencecited by 3
- 2025Privacy of Groups in Dense Street Imagerycited by 1
- 2025Automated Building Heritage Assessment Using Street-Level Imagerycited by 1
- 2025Vehicle detection from GSV imagery: Predicting travel behaviour for cycling and motorcycling using Computer Visioncited by 0
- 2025MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understandingcited by 0
- 2025Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implicationscited by 0
- 2026A Contrastive Learning Framework Empowered by Attention-based Feature Adaptation for Street-View Image Classificationcited by 0
- 2026A Contrastive Learning Framework Empowered by Attention-based Feature Adaptation for Street-View Image Classification18 Feb 2026
- 2025Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications22 Dec 2025
- 2025MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding19 Dec 2025
- 2025Vehicle detection from GSV imagery: Predicting travel behaviour for cycling and motorcycling using Computer Vision18 Aug 2025
- 2025Automated Building Heritage Assessment Using Street-Level Imagery15 Aug 2025
- 2025Privacy of Groups in Dense Street Imagery11 May 2025
- 2025OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery1 Apr 2025
- 2024ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science24 Dec 2024
- 2024Coverage and Bias of Street View Imagery in Mapping the Urban Environment22 Sep 2024
- 2024A citizen science toolkit to collect human perceptions of urban environments using open street view images29 Feb 2024