AI-based Digital Documentation as Sensors of Heritage Morphological Value: Recording Plane Forms of Historical Rural Settlements Hierarchically

Abstract. Historical Rural Settlements (HRS) are recognised by ICOMOS-IFLA as tangible cultural heritage, where plane forms preserve multi-layered morphological value accrued over successive eras. These plane forms not only reflect past settlement planning and community interactions but also reveal contrasts between vestigial historical cores and more recent modern developments. Although digital documentation has become a mainstream conservation approach, it often overlooks the interpretive dimension that connects raw geometric data with cultural significance. To address this gap and manage the complexity of large-scale image acquisition, annotation, and analysis, this study treats image-based artificial intelligence (AI) as a cultural sensor for HRS morphology. A re-peatable, open-source workflow that integrates high-resolution remote sensing, expert-driven annotation, hierarchical segmentation (Mask R-CNN), and building(polygon) regularisation was proposed. In the Taihang Baxing (THBX) region of China, 778 HRS samples were processed through a two-stage model with several supportive techniques: the first differentiates historical and modern regions, and the second extracts building footprints within each region. Validation on THBX demonstrates reliable differentiation of hierarchical plane forms and efficient generation of a vectorised digital documentation dataset. By embedding geospatial registra-tion and building regularisation tools, the workflow ensures downstream usability for use cases like geospatial statistics, quantitative morphology and evolutionary mechanisms, providing insights for the rural planning consequently. The proposed workflow emphas-ises heritage morphological value and digital documentation as guiding principles, demonstrating that AI as technical agents can perceptually interpret cultural significance. It can serve as a tool for extracting and highlighting the historical value of HRS in the formulation of conservation strategies and development plans, contributing to sustainable and inclusive rural heritage management.

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Publication Details

Journal
˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
Published
2025-10-02
DOI
https://doi.org/10.5194/isprs-archives-xlviii-m-9-2025-1029-2025
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
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article

AI-based Digital Documentation as Sensors of Heritage Morphological Value: Recording Plane Forms of Historical Rural Settlements Hierarchically

Sinan Yuan, Nanshuo Mu, Ying Xiao, Yanru Yin et al.
˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
3D Surveying and Cultural Heritage
article

AI-based Digital Documentation as Sensors of Heritage Morphological Value: Recording Plane Forms of Historical Rural Settlements Hierarchically

Sinan Yuan, Nanshuo Mu, Ying Xiao, Yanru Yin, H. Fu
article en

Abstract

Abstract. Historical Rural Settlements (HRS) are recognised by ICOMOS-IFLA as tangible cultural heritage, where plane forms preserve multi-layered morphological value accrued over successive eras. These plane forms not only reflect past settlement planning and community interactions but also reveal contrasts between vestigial historical cores and more recent modern developments. Although digital documentation has become a mainstream conservation approach, it often overlooks the interpretive dimension that connects raw geometric data with cultural significance. To address this gap and manage the complexity of large-scale image acquisition, annotation, and analysis, this study treats image-based artificial intelligence (AI) as a cultural sensor for HRS morphology. A re-peatable, open-source workflow that integrates high-resolution remote sensing, expert-driven annotation, hierarchical segmentation (Mask R-CNN), and building(polygon) regularisation was proposed. In the Taihang Baxing (THBX) region of China, 778 HRS samples were processed through a two-stage model with several supportive techniques: the first differentiates historical and modern regions, and the second extracts building footprints within each region. Validation on THBX demonstrates reliable differentiation of hierarchical plane forms and efficient generation of a vectorised digital documentation dataset. By embedding geospatial registra-tion and building regularisation tools, the workflow ensures downstream usability for use cases like geospatial statistics, quantitative morphology and evolutionary mechanisms, providing insights for the rural planning consequently. The proposed workflow emphas-ises heritage morphological value and digital documentation as guiding principles, demonstrating that AI as technical agents can perceptually interpret cultural significance. It can serve as a tool for extracting and highlighting the historical value of HRS in the formulation of conservation strategies and development plans, contributing to sustainable and inclusive rural heritage management.

˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesVol. XLVIII-M-9-2025
Ministry of Culture (IN), Tianjin University (CN), Harbin Institute of Technology (CN), University College London (GB)
Openalex Percentile: Top 63%
3D Surveying and Cultural Heritage
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