Author Information
Sutat Gammanee, Kanchanaburi Rajabhat University, ThailandWarong Naivinit, Kanchanaburi Rajabhat University, Thailand
Chanakit Mitsongkore, Kanchanaburi Rajabhat University, Thailand
Sureewan Jangjit, Kanchanaburi Rajabhat University, Thailand
Abstract
The integrity of cultural data is fundamental to the preservation of local heritage, the formulation of evidence-based policies, and the advancement of cultural tourism. In Thailand, where cultural diversity is both rich and deeply embedded in community life, accurate and contextually relevant cultural information is indispensable for fostering local identity and driving grassroots economic development. Nevertheless, the existing Cultural Mapping System in Thailand continues to encounter persistent issues, including data inconsistency, duplication, incomplete metadata, and limited contextual alignment, all of which undermine its practical and policy-oriented applications. This study introduces an artificial intelligence (AI)-enabled framework for data quality control within the national Cultural Mapping platform. The proposed system leverages advanced AI techniques, including image classification, natural language processing, and geospatial validation to systematically detect anomalies, assess content relevance, and generate automated recommendations for data refinement. A pilot implementation using approximately 6,000 cultural records demonstrated that the system achieved over 85% accuracy in identifying irrelevant or duplicate entries, thereby significantly alleviating the burden of manual data verification. Moreover, the system supports the temporal and value chain-based visualization of cultural data, facilitating both operational decision-making and long-term strategic planning. The findings underscore the potential of AI technologies to enhance the quality, usability, and trustworthiness of national cultural datasets, contributing to the broader goal of intelligent, data-driven cultural governance in Thailand.
Paper Information
Conference: KAMC2025Stream: Cultural Studies
This paper is part of the KAMC2025 Conference Proceedings (View)
Full Paper
View / Download the full paper in a new tab/window
To cite this article:
Gammanee S., Naivinit W., Mitsongkore C., & Jangjit S. (2026) AI-Based Data Quality Control for Cultural Mapping Systems: A Case Study From Thailand ISSN: 2436-0503 – The Kyoto Conference on Arts, Media & Culture 2025: Official Conference Proceedings (pp. 575-585) https://doi.org/10.22492/issn.2436-0503.2025.47
To link to this article: https://doi.org/10.22492/issn.2436-0503.2025.47
Comments
Powered by WP LinkPress