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

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Image Analysis and Processing. ICIAP 2022 Workshops (ICIAP 2022)

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.

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Notes

  1. 1.

    https://github.com/ayoolaolafenwa/PixelLib.

  2. 2.

    https://assetstore.unity.com/3d/environments.

  3. 3.

    https://www.google.com/forms/about/.

  4. 4.

    In this work, we experimented with free-text comments that were apriori about spaces, as stated in Sect. 4. Therefore, we left other challenges such as the separation of texts about spaces and happenings in spaces for future research.

  5. 5.

    The concept “rotonda” is taken from user describing nearby roundabout which is not captured on the image.

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Acknowledgments.

This work was supported by the EC-funded research and innovation programme H2020 Mindspaces: “Art-driven adaptive outdoors and indoors design” under the grant agreement No.825079.

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Correspondence to Theodora Pistola .

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Pistola, T. et al. (2022). Imageability-Based Multi-modal Analysis of Urban Environments for Architects and Artists. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_18

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  • DOI: https://doi.org/10.1007/978-3-031-13321-3_18

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