Digitally Decoding Heritage: Analyzing the Sellman Tenant House Through HBIM and Digital Documentation Techniques

This study presents a comprehensive digital documentation and preservation effort for the Sellman Tenant House, a historic structure once part of the 18th-century Sellman Plantation in Maryland, USA. This research employs an array of digital technologies, including Terrestrial Laser Scanning (TLS), digital photogrammetry, Unmanned Aerial Vehicles (UAVs), 3D virtual tours, and Heritage Building Information Modeling (HBIM), to document and analyze the construction techniques and historical evolution of the house. Given the absence of written records detailing its original construction, this study utilizes data from these digital documentation methods to explore the building’s structure and determine its construction timeline and methods. Additionally, this research investigates the potential of HBIM as an educational platform to enhance public understanding of heritage buildings by creating interactive and accessible digital models. The findings highlight the effectiveness of combining digital tools to decode vernacular construction and showcase the potential of HBIM in preserving and interpreting historic buildings for diverse audiences, especially for educational purposes. This research contributes to the growing field of digital heritage preservation by showcasing a case study of integrating multiple digital technologies to study, preserve, and promote understanding of a culturally significant yet understudied structure.

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Publication Details

Journal
Virtual Worlds
Published
2025-03-18
DOI
https://doi.org/10.3390/virtualworlds4010010
Citations
1
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
2.35
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article

Digitally Decoding Heritage: Analyzing the Sellman Tenant House Through HBIM and Digital Documentation Techniques

Botao Li, Junshan Liu, Danielle S. Willkens, Shadi Alathamneh et al.
1 citations
Virtual Worlds
3D Surveying and Cultural Heritage
2.35
article

Digitally Decoding Heritage: Analyzing the Sellman Tenant House Through HBIM and Digital Documentation Techniques

Botao Li, Junshan Liu, Danielle S. Willkens, Shadi Alathamneh, Sehhoon Park
article en
1 citations

Abstract

This study presents a comprehensive digital documentation and preservation effort for the Sellman Tenant House, a historic structure once part of the 18th-century Sellman Plantation in Maryland, USA. This research employs an array of digital technologies, including Terrestrial Laser Scanning (TLS), digital photogrammetry, Unmanned Aerial Vehicles (UAVs), 3D virtual tours, and Heritage Building Information Modeling (HBIM), to document and analyze the construction techniques and historical evolution of the house. Given the absence of written records detailing its original construction, this study utilizes data from these digital documentation methods to explore the building’s structure and determine its construction timeline and methods. Additionally, this research investigates the potential of HBIM as an educational platform to enhance public understanding of heritage buildings by creating interactive and accessible digital models. The findings highlight the effectiveness of combining digital tools to decode vernacular construction and showcase the potential of HBIM in preserving and interpreting historic buildings for diverse audiences, especially for educational purposes. This research contributes to the growing field of digital heritage preservation by showcasing a case study of integrating multiple digital technologies to study, preserve, and promote understanding of a culturally significant yet understudied structure.

Virtual WorldsVol. 4(1)
Smithsonian Institution (US), Georgia Institute of Technology (US), Auburn University (US)
Openalex Percentile: Top 15%
3D Surveying and Cultural Heritage
2.35
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Digitally Decoding Heritage: Analyzing the Sellman Tenant House Through HBIM and Digital Documentation Techniques — Botao Li, Junshan Liu, et al. · Virtual Worlds (2025) | TGRS Research Map | TGRS