Evaluating the Quality of Open Building Datasets for Mapping Urban Inequality: A Comparative Analysis Across 5 Cities

TL;DR

This study assesses the quality and biases of AI-generated open building datasets across five global cities, revealing significant variance and highlighting implications for urban planning and resource allocation.

Contribution

It provides a comparative analysis of AI-generated building datasets against OpenStreetMap across diverse urban environments, highlighting data quality issues and socio-economic indicators.

Findings

Houston and Berlin show high data alignment and completeness.

Cities like Accra and Caracas are under-represented in datasets.

Building size distributions reflect socio-economic divides.

Abstract

While informal settlements lack focused development and are highly dynamic, the quality of spatial data for these places may be uncertain. This study evaluates the quality and biases of AI-generated Open Building Datasets (OBDs) generated by Google and Microsoft against OpenStreetMap (OSM) data, across diverse global cities including Accra, Nairobi, Caracas, Berlin, and Houston. The Intersection over Union (IoU), overlap analysis and a positional accuracy algorithm are used to analyse the similarity and alignment of the datasets. The paper also analyses the size distribution of the building polygon area, and completeness using predefined but regular spatial units. The results indicate significant variance in data quality, with Houston and Berlin demonstrating high alignment and completeness, reflecting their structured urban environments. There are gaps in the datasets analysed, and cities like Accra and Caracas may be under-represented. This could highlight difficulties in capturing complex or informal regions. The study also notes different building size distributions, which may be indicative of the global socio-economic divide. These findings may emphasise the need to consider the quality of global building datasets to avoid misrepresentation, which is an important element of planning and resource distribution.