Automatic Extraction and Linkage between Textual and Spatial Data for Architectural Heritage

计算机科学 功能可见性 过程(计算) 空间分析 人机交互 情报检索 地理 遥感 操作系统
作者
Sun-Young Jang,Sung-Ah Kim
出处
期刊:Journal on computing and cultural heritage [Association for Computing Machinery]
卷期号:16 (3): 1-19 被引量:1
标识
DOI:10.1145/3586158
摘要

Recent developments in experience technologies such as augmented reality (AR)/virtual reality (VR) have facilitated receiving content about the audience on site and experiencing architectural heritage in a virtual space. Despite the development of experience devices, if the quantity and quality of content are not sufficiently supported, then immersive user experiences are bound to be limited. Considerable amounts of money, manpower, and time are required to make a building into experience content. Tasks such as building a database create experiential content that occupies a large proportion of the overall process. Therefore, it is necessary to devise an automated method for building data, which is the basis for content creation. This study extracted data on architectural heritage automatically and structured it around spatial expression so it can function as base work for mass content creation. Specifically, this study devised a method to link and structure text and spatial data centering on the architectural spatial data model. Text and spatial data were extracted automatically using deep learning, and each derived result was mapped to Indoor Affordance Spaces—an indoor spatial data model—to test whether information inference is possible based on the interconnection relationship. The spatial experience route inferred using the data model expresses the detailed area where the viewing element exists, based on the description method of the model. It also shows the process of reconstructing an efficient movement line with topological relationships between spaces. The series of processes presented herein showed sufficient applicability to the extraction of data and the connection and utilization of data models. This is useful for extracting and classifying information used for content from massive raw data. This study also considered the specificity arising from architectural heritage and spatial information. Therefore, the research concept can be applied in exhibition and experience spaces, such as architectural heritage, museums, and art galleries, to create sources for content creation and refer to content composition.
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