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

arXiv: https://arxiv.org/abs/2403.00174 · HTML: https://mlsanity.com/paper/2403.00174/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Semantic Segmentation using Vision Transformers: A survey](https://mlsanity.com/paper/2305.03273/) (2023)
- [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016)
- [Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization](https://mlsanity.com/paper/2302.00275/) (2023)
- [To use or not to use proprietary street view images in (health and place) research? That is the question](https://mlsanity.com/paper/2402.11504/) (2024)

## Built on by (most cited first, 9 of 9)

- [Coverage and Bias of Street View Imagery in Mapping the Urban Environment](https://mlsanity.com/paper/2409.15386/) (2024)
- [Urban Safety Perception Assessments via Integrating Multimodal Large Language Models with Street View Images](https://mlsanity.com/paper/2407.19719/) (2024)
- [OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery](https://mlsanity.com/paper/2504.02866/) (2025)
- [ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science](https://mlsanity.com/paper/2412.18641/) (2024)
- [Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity](https://mlsanity.com/paper/2508.11708/) (2025)
- [From Drone Imagery to Livability Mapping: AI-powered Environment Perception in Rural China](https://mlsanity.com/paper/2508.21738/) (2025)
- [URBAN-SPIN: A street-level bikeability index to inform design implementations in historical city centres](https://mlsanity.com/paper/2602.10124/) (2026)
- [Cross-View Urban Sensing: Mapping Subjective Streetscape Perception via AlphaEarth Embeddings and Urban Context](https://mlsanity.com/paper/2608.16310/) (2026)
- [From Pixels to Policy: A Multi-Agent System for Intervention and Geo-Spatial Decision Support](https://mlsanity.com/paper/2610.01870/) (2026)

## 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.