Imageability-Based Multi-modal Analysis of Urban Environments for Architects and Artists

Abstract

According to urban planner Kevin Lynch, imageability is the ability of a physical object to evoke a strong image in any viewer, making it memorable. The concept of imageability is important for architects and urban designers, so that their creations meet the needs of the citizens and improve the aesthetics of the place. Recently, computer vision and textual analysis techniques have been investigated for calculating the imageability of a place. In this paper, we propose a novel multi-modal system that utilises both visual and textual analysis methods to estimate the imageability score of a place. In addition, an image sentiment analysis deep learning model had been developed to provide supplementary information about the sentiment that is evoked to citizens by urban locations. Finally, a text generation algorithm is used to provide an explanation of the information extracted by the data analysis in a form of text to facilitate the works of architects and urban designers.