Sustainable artificial intelligence for historic building components classification
A case of Unesco world heritage site in Congonhas, Brazil
Datos Bibliográficos
| ID | 19482393 |
|---|---|
| Autores | André Luiz Carvalho Ottoni (0000-0003-2136-9870, Union zur Förderung von Oel- und Proteinpflanzen e.V), Lara Toledo Cordeiro Ottoni (0000-0003-3996-431X, Instituto Federal de Educação Ciência e Tecnologia do Norte de Minas Gerais) |
| Año | 2026 |
| Páginas | 1-16 |
| Fecha de publicación | 2026-05-19 |
| Peer Reviewed | Sí |
| Open Access | No |
| Tipo | ARTICLE |
| Revista | Journal of Cultural Heritage Management and Sustainable Development (JOURNAL) |
| Identificadores de la revista | ISSN: 2044-1266 • E-ISSN: 2044-1274 |
| Editorial | Emerald (PUBLISHER) |
| DOI | 10.1108/jchmsd-05-2025-0145 |
| OpenAlex | W7161598220 |
| Idioma | EN |
| Referencias citadas | 21 |
Purpose This study aims to propose a sustainable artificial intelligence (AI) approach for the historic building components classification. To this end, the energy efficiency of deep learning models is investigated in the detection of key architectural elements of churches in the Historic Town of Congonhas, Brazil. In addition, practical tests are conducted at the Sanctuary of Bom Jesus, a significant Brazilian monument and UNESCO World Heritage Site. Design/methodology/approach The methodology proposed in this paper consists of five main stages: (1) data collection through the capture of photographs of cultural heritage buildings,(2) dataset organization for training, validation, and testing experiments, (3) selection of six traditional deep learning models from the literature, (4) design of experiments for simulation and real-world testing and (5) sustainable artificial intelligence calculations to assess the energy efficiency of the deep learning models. Findings The results demonstrate that it is possible to conduct experiments for historic building component classification using more energy-efficient computational models, such as MobileNet and MobileNetV2. In other words, these models require less energy for training the artificial intelligence. Furthermore, the sustainable AI models achieved accuracy levels comparable to those of more energy-intensive structures. Originality/value This study presents an innovative contribution through a comprehensive analysis of energy consumption for historic building component detection using computer vision. Additionally, the case study involving sustainable artificial intelligence applied to a UNESCO World Heritage Site in Congonhas represents a novel approach in the recent literature
Applications of artificial intelligence · Cultural heritage · Deep learning · Efficient energy use · Energy consumption · Sustainable development · World heritage · 3D Surveying and Cultural Heritage · Building Energy and Comfort Optimization · Conservation Techniques and Studies
Deep learning
New technologies for the conservation and preservation of cultural heritage through a bibliometric analysis
Evaluating the management of ethnic minority heritage and the use of digital technologies for learning
Measuring the performance of expert-based evaluation method (Ebem) used for listing and grading heritage buildings
An exploration in digital techniques to read Ainu textile patterns
Machine learning techniques for structural health monitoring of heritage buildings
The Classification of Cultural Heritage Buildings in Athens Using Deep Learning Techniques
ImageOP
Artificial Intelligence at the Interface between Cultural Heritage and Photography
Artificial intelligence-assisted visual inspection for cultural heritage
Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings
CNN-based statistics and location estimation of missing components in routine inspection of historic buildings
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |