mlsanityData to 7 Oct 2026

A citizen science toolkit to collect human perceptions of urban environments using open street view images

arXiv 2403.00174 ↗First posted 29 Feb 2024cs.CVcited by 9

Not on the map. Why?

Abstract (arXiv)

Street View Imagery (SVI) is a valuable data source for studies (e.g., environmental assessments, green space identification or land cover classification). While commercial SVI is available, such providers commonly restrict copying or reuse in ways necessary for research. Open SVI datasets are readily available from less restrictive sources, such as Mapillary, but due to the heterogeneity of the images, these require substantial preprocessing, filtering, and careful quality checks. We present an efficient method for automated downloading, processing, cropping, and filtering open SVI, to be used in a survey of human perceptions of the streets portrayed in these images. We demonstrate our open-source reusable SVI preparation and smartphone-friendly perception-survey software with Amsterdam (Netherlands) as the case study. Using a citizen science approach, we collected from 331 people 22,637 ratings about their perceptions for various criteria. We have published our software in a public repository for future re-use and reproducibility.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 8 of its 65 references that are in the map. First posted on arXiv 29 Feb 2024.

Builds on

8 of 65 references.

References · 8

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  3. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
  4. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  5. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73
  6. 2016Deep Learning the City : Quantifying Urban Perception At A Global Scalecited by 65
  7. 2023Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalizationcited by 41
  8. 2024To use or not to use proprietary street view images in (health and place) research? That is the questioncited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 3.

9 papers.