Predicting Household Income with Sentinel and Street View Imagery: A Comparative Study across Amsterdam, Sydney, and New York

• Free-cost Sentinel imagery is applicable for predicting income with proxies. • SHAP analysis revealed spectral-textural spatial signatures of income. • There are common spatial landscape indicators of income across cities. • Google Street View-based visual indicators can be used complementarily. In the context of urbanisation and growing disparities, timely and detailed spatial data on income inequality in cities is essential. We combined satellite imagery with streetlevel photographs provided by Google Street View to reveal the spatial distribution of household income. For this, we suggest a harmonised framework for median household income modelling based on deconstructing landscape patterns using a machine-learning approach, applied across three ’global cities’: Amsterdam, New York, and Sydney. First, we classified Sentinel-1 and Sentinel-2 mosaics and Google Street View scenes to detect functional elements of the built environment. Second, we calculated spatial indices for Sentinel imagery and visual indices for Google Street View scenes to characterise the urban landscape. Third, by combining various indicators, we trained city-specific income prediction models according to ground truth census data. The correlation between actual and predicted income in New York and Sydney reached 0.76 and 0.78, respectively. The accuracy of income prediction in Amsterdam reached 51.13%. We revealed relationships between spatial indicators of landscape patterns and spatial income distribution and recommend using Sentinel-1 and Sentinel-2 imagery as the primary data choice for income modelling in datarestricted regions. Google Street View data can be used complementarily when available.