Graph Neural Network Based Living Comfort Prediction Using Real Estate Floor Plan Images
Document Type
Conference Proceeding
Publication Date
12-13-2022
Abstract
In recent years, machine learning has been widely used in the real estate field. However, most of these previous studies have been limited to analysis based on objective perspectives, such as analysis of the structure of the floor plan and rent estimation. On the other hand, we focus on the subjective "living comfort"of real estate properties and aim to predict people's impressions of properties based on information obtained from floor plan images. Specifically, by using deep learning to analyze floor plan images and graph structures reflecting the floor plans, it becomes possible to predict the attractiveness of each property in terms of spaciousness, modernity, privacy, and so on. As a result of the experiments, the effectiveness of using both the floor plan image and the corresponding graph structure for prediction was confirmed.
Identifier
85145769562 (Scopus)
ISBN
[9781450394789]
Publication Title
Proceedings of the 4th ACM International Conference on Multimedia in Asia Mmasia 2022
External Full Text Location
https://doi.org/10.1145/3551626.3564970
Fund Ref
University of Tokyo
Recommended Citation
Kitabayashi, Ryota; Narahara, Taro; and Yamasaki, Toshihiko, "Graph Neural Network Based Living Comfort Prediction Using Real Estate Floor Plan Images" (2022). Faculty Publications. 2408.
https://digitalcommons.njit.edu/fac_pubs/2408
