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This is a Quantitative Study study.

Harmonizing street-view semantics and spatial predictors for dominant urban visual composition modelling

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This framework evaluates visual dominance in urban areas using street-view images and spatial predictors, highlighting challenges in accuracy.

A framework was developed to model urban visual dominance using street-level images and semantic data.
Utilizing 12,631 street-view panoramas, the study achieved an accuracy of 0.503 for visual dominance classes.
The best performance came from a logistic regression model that combined multiple spatial predictors and visual data.
Findings indicate that while there are promising approaches to generalizing visual data, accuracy varies significantly across environments.

Pradana et al. (2025) studied this question.

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This framework evaluates visual dominance in urban areas using street-view images and spatial predictors, highlighting challenges in accuracy.