# Deep Learning the City : Quantifying Urban Perception At A Global Scale

arXiv: https://arxiv.org/abs/1608.01769 · HTML: https://mlsanity.com/paper/1608.01769/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2016.

## Built on by (most cited first, 30 of 65)

- [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021)
- [Learning to Compare Longitudinal Images](https://mlsanity.com/paper/2304.02531/) (2023)
- [Global urban visual perception varies across demographics and personalities](https://mlsanity.com/paper/2505.12758/) (2025)
- [A citizen science toolkit to collect human perceptions of urban environments using open street view images](https://mlsanity.com/paper/2403.00174/) (2024)
- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
- [Urban Safety Perception Assessments via Integrating Multimodal Large Language Models with Street View Images](https://mlsanity.com/paper/2407.19719/) (2024)
- [EMPLACE: Self-Supervised Urban Scene Change Detection](https://mlsanity.com/paper/2503.17716/) (2025)
- [UrbanFeel: A Comprehensive Benchmark for Temporal and Perceptual Understanding of City Scenes through Human Perspective](https://mlsanity.com/paper/2509.22228/) (2025)
- [Generative agents in the streets: Exploring the use of Large Language Models (LLMs) in collecting urban perceptions](https://mlsanity.com/paper/2312.13126/) (2023)
- [MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layouts](https://mlsanity.com/paper/2511.20415/) (2025)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery](https://mlsanity.com/paper/2305.02911/) (2023)
- [Do Vision-Language Models See Urban Scenes as People Do? An Urban Perception Benchmark](https://mlsanity.com/paper/2509.14574/) (2025)
- [Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data](https://mlsanity.com/paper/2103.05107/) (2021)
- [Computer vision-based analysis of buildings and built environments: A systematic review of current approaches](https://mlsanity.com/paper/2208.00881/) (2022)
- [URSimulator: Human-Perception-Driven Prompt Tuning for Enhanced Virtual Urban Renewal via Diffusion Models](https://mlsanity.com/paper/2409.14589/) (2024)
- [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)
- [Urban-R1: Reinforced MLLMs Mitigate Geospatial Biases for Urban General Intelligence](https://mlsanity.com/paper/2510.16555/) (2025)
- [Computer vision-enriched discrete choice models, with an application to residential location choice](https://mlsanity.com/paper/2308.08276/) (2023)
- [From Efficiency to Equity: Measuring Fairness in Preference Learning](https://mlsanity.com/paper/2410.18841/) (2024)
- [How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images](https://mlsanity.com/paper/2508.21565/) (2025)
- [On the Sample Complexity of Rank Regression from Pairwise Comparisons](https://mlsanity.com/paper/2105.01463/) (2021)
- [City-Wide Perceptions of Neighbourhood Quality using Street View Images](https://mlsanity.com/paper/2211.12139/) (2022)
- [AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing](https://mlsanity.com/paper/2308.07580/) (2023)
- [Climate-sensitive Urban Planning through Optimization of Tree Placements](https://mlsanity.com/paper/2310.05691/) (2023)
- [Artificial Intelligence in Landscape Architecture: A Survey](https://mlsanity.com/paper/2408.14700/) (2024)
- [AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces](https://mlsanity.com/paper/2411.00956/) (2024)
- [Visual Chronicles: Using Multimodal LLMs to Analyze Massive Collections of Images](https://mlsanity.com/paper/2504.08727/) (2025)
- [Artifacts of Idiosyncracy in Global Street View Data](https://mlsanity.com/paper/2505.11046/) (2025)

## Abstract (arXiv)

Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city's physical appearance and the behavior and health of its residents. Yet, the throughput of current methods is too limited to quantify the perception of cities across the world. To tackle this challenge, we introduce a new crowdsourced dataset containing 110,988 images from 56 cities, and 1,170,000 pairwise comparisons provided by 81,630 online volunteers along six perceptual attributes: safe, lively, boring, wealthy, depressing, and beautiful. Using this data, we train a Siamese-like convolutional neural architecture, which learns from a joint classification and ranking loss, to predict human judgments of pairwise image comparisons. Our results show that crowdsourcing combined with neural networks can produce urban perception data at the global scale.