Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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Evaluating Store Image and Creating Positioning Maps Based on Deep Learning - Focused on the Interior Environments of Coffee Shop Brands -

딥러닝 기반 점포 이미지 평가 및 포지셔닝 맵 생성 - 커피 전문점 브랜드의 실내공간을 중심으로 -

  • Han, Yoojin (Dept. of Interior Architecture and Built Environment, Yonsei University) ;
  • Lee, Hyunsoo (Dept. of Interior Architecture and Built Environment, Yonsei University)
  • 한유진 (연세대 실내건축학과 ) ;
  • 이현수 (연세대학교 실내건축학과 )
  • Received : 2022.12.31
  • Accepted : 2023.02.02
  • Published : 2023.02.28
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Abstract

This study presents a deep learning approach to measuring a brand's store image while generating positioning maps using social media data. Store design and architecture were highlighted as effective communicators of brand identity and positioning, but the spatial environment had been solely studied using traditional approaches such as surveys. This study adopted deep learning based CNN, which is an alternative methodology for evaluating a brand's store image and created a positioning map in terms of interior design. Two axes were set to create a positioning map of style (X) and atmosphere (Y) that collected training data from Pinterest. Using the training dataset, this research employed Inception-V3 to retrain this model to evaluate the interior design. Based on the retrained model, the interior images of coffee shop brands were evaluated to determine each brand's position and create a positioning map. Another positioning map was created based on a conventional method via a survey to demonstrate the validity of this approach. The results demonstrated that a brand's store image can be trained and recognized using social data and deep learning technology. Additionally, brands' relative positions and relationships can be assessed through a deep learning framework; therefore, a brand positioning map can be created. Various applications of these approaches in decision-making for brand store design, including the assessment of brand store positioning and redesigning stores were highlighted. Lastly, this study suggests wider uses for social big data and deep learning technology in branding and architectural design.

Keywords

Acknowledgement

이 논문은 2022년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임 (NRF-2022R1A6A3A01087469).

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