Artificial Intelligence for Routine Heritage Monitoring and Sustainable Planning of the Conservation of Historic Districts: A Case Study on Fujian Earthen Houses (Tulou)

Abstract

With its advancements in relation to computer science, artificial intelligence has great potential for protecting and researching the world heritage Fujian earthen houses (Tulou) historical district. Wood is an important material used in the construction of Fujian earthen houses (Tulou); wood is used in both the main structure of the buildings and for decoration. However, professionals must invest significant time and energy in evaluating any damage before repairing a building. In this context, this study proposes and optimizes a detection method based on the YOLOv8 model for detecting damage to the wooden structure of Fujian earthen houses. Through multiple experiments and adjustments, we gradually improved the detection performance of the model and verified its effectiveness and reliability in practical applications. The main results of this study are as follows: (1) This machine-learning-based object detection method can efficiently and accurately identify damaged contents, overcoming the limitations of traditional evaluation methods in terms of labor and time costs. This approach will aid in the daily protection monitoring of historical districts and serves as a preliminary method for their renewal and restoration. (2) Through multiple rounds of experiments, we optimized the YOLOv8 model and significantly improved its detection accuracy and stability by removing samples with complex backgrounds, improving label quality, and adjusting hyperparameters. In the final experiment, the model’s overall mAP was only 57.00% at most. However, during the field test, the model successfully identified nearly all damage points, including holes, stains, and cracks in the wooden structure of the analyzed earthen building, effectively fulfilling the requirements of the detection task. (3) In the KuiJu Lou field test in Fujian Tulou, the model also performed well in complex environments and was able to reliably detect damage types such as holes, stains, and cracks in the wooden structure. This test confirmed the model’s efficiency and stability in practical applications and provided reliable technical support for Fujian Tulou protection and restoration.

Keywords

• machine learning
• world heritage
• YOLOv8
• historic district protection
• earthen houses (tulou)
• Fujian