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Products for grant HAA-258763-18

HAA-258763-18
Digital Floor Plan Database: A New Method for Analyzing Architecture
Elise King, Baylor University

Grant details: https://securegrants.neh.gov/publicquery/main.aspx?f=1&gn=HAA-258763-18

The Building Database and Analytics System (BuDAS): Computer science and interior design take on ‘big data' (Conference Paper/Presentation) [show prizes]
Title: The Building Database and Analytics System (BuDAS): Computer science and interior design take on ‘big data'
Author: Elise King
Author: Qiannan Wu
Author: David Lin
Abstract: Mass digitization has allowed greater access to archival materials than ever before. While access has certainly improved, processing and analyzing the rich information contained in these drawings remains challenging. In the age of ‘big data,’ having access to information is no longer enough; we need tools to sift through massive amounts of raw information to identify meaning and pattern (Manovich, 2012). Frustrated by the lack of tools to analyze archival floor plans, the authors considered the following question: How can we automate the collection of floor plan information and explore patterns and relationships between plans, architects/designers, and time periods? In this paper we explore the challenges of analyzing archival floor plans and provide an overview of the Building Database & Analytics System (BuDAS), which we developed to address these problems. (abbreviated abstract)
Date: 10/03/19
Primary URL: http://www.idec.org/i4a/pages/index.cfm?pageid=4719
Conference Name: Interior Design Educators Council Southwest Regional Conference

Building Database Analytics System (BuDAS): Examining challenges to floor plan detection (Conference Paper/Presentation)
Title: Building Database Analytics System (BuDAS): Examining challenges to floor plan detection
Author: Elise King
Author: David Lin
Abstract: The success of plan recognition is determined by two main factors: 1). quality of the plan image, and 2). interpretation of the plan information. Plan image quality includes factors such as clarity, contrast, and digital noise. A high-quality plan image has high contrast, minimal noise, and clear lines and text. Plan interpretation is essentially how well the system is able to process and make sense of the layers of plan information. A basic floor plan that includes walls, windows, doors, and room labels is easier for the system to interpret than a plan with additional layers of information, such as furniture, dimension stringers, material symbols, grids, landscaping, and ceilings or rooflines. These additional layers of information can result in the plan detection misidentifying other lines as walls, for example. By testing each of these variables, we are able to determine how to improve plan for users. This poster will explore the results of these tests and suggest areas for improvement. (abbreviated abstract)
Date: 10/26/19
Conference Name: Digital Frontiers Conference


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