# Whose city is it? Mapping perceived urban livability with citizen-guided AI

## TL;DR

This paper introduces an AI approach to map urban livability by incorporating the perspectives of deprived urban area residents and city planners in Ghana.

## Contribution

The novel contribution is the development of 'AI-voters' that use citizen input to reduce data needs and reveal spatial inequities in urban planning.

## Key findings

- Using AI-voters reduced data requirements for mapping urban livability by 90% in the Greater Accra Metropolitan Area.
- Planners assigned higher livability scores and overlooked DUA residents' preferences, such as avoiding coastal exposure.
- AI-voters trained on DUA residents' preferences accurately mirrored human behavior based on urban features like greenery and building density.

## Abstract

Urban livability is shaped by dominant values, often economic or aesthetic, and power dynamics that often overlook the lived experiences of deprived urban area (DUA) residents. As a result, conventional livability indicators risk reinforcing existing inequalities unless these are grounded in inclusive and participatory approaches. To address this issue, we developed lightweight deep learning models – ‘AI-voters’ – trained on livability preferences from both DUA residents and city planners, using open-source satellite imagery. Applied in Ghana’s Greater Accra Metropolitan Area, our approach reduced data requirements to map urban livability by 90% through a two-step urban form sampling strategy that enabled scalable participatory mapping. Training separate ‘AI-voters’ for planners and DUA residents revealed systematic differences: planners not only disagree among themselves but also consistently assign higher livability scores and overlook the preferences of DUA residents, such as avoiding coastal area exposure. The AI-voters mirrored human-voter behavior based on physical urban features such as greenery and building density, especially when trained on the preferences of DUA residents, demonstrating their potential as scalable proxies for local insights. These results highlight the importance of integrating community perspectives into AI models trained to map urban livability to expose hidden spatial inequities and promote more inclusive urban development.