Automated Wall Moisture Detection in Heritage Sites Based on Convolutional Neural Network (CNN) for Infrared Imagery  
[PERSON]  
which can lead to various degrees of damage and ultimately shorten the lifespan of the building [3]. Various techniques can be employed to analyze the moisture conditions in brick walls. Given the fragility and vulnerability of cultural heritage structures, invasive sampling methods may cause irreversible damage. Therefore, nondestructive testing (NDT) techniques are prioritized in heritage conservation. In recent years, NDT methods have become the primary approach for the assessing moisture, defects, and deterioration of heritage sites [4]. The commonly used techniques include infrared thermography (IRT) [5], electrical conductivity (EC), electrical resistivity tomography (ERT), and ground-penetrating radar (GPR). These methods provide valuable information about both the internal structure and surface conditions of heritage materials. Among them, IRT has been proven effective for rapidly assessing the moisture distribution on building surfaces and facades through long-term applications [6]. Due to its suitability for evaluating the moisture content in buildings [7], IRT has been widely adopted for the facade diagnostics of historical monuments around the world, such as the Sao Paulo Municipal Market [8], Hagia Sophia [9], and the Pitti Palace in Florence [10]. In the context of cultural heritage studies, wall moisture is commonly categorized into five types: accidental, air, building, percolation, and upward moisture. Accidental moisture refers to water unintentionally entering a structure due to failures or defects in piping systems, such as rainwater drainage, sewage, or drinking water lines. Air moisture occurs when high ambient humidity interacts with surfaces cooler than the dew point, leading to condensation. Building moisture denotes water trapped within the materials during the wall construction process. Percolation moisture refers to water infiltration through cracks, structural defects, or openings such as doors and windows. Lastly, upward moisture involves the capillary rise of groundwater, which may vary seasonally based on humidity levels. Thermal infrared (IR) imaging enables the identification of temperature differences on surfaces made of the same material [11], with moisture-affected areas typically appearing cooler than the surrounding dry regions. As a result, thermal imaging can assist in distinguishing moisture sources, prioritizing restoration efforts, and formulating conservation strategies for cultural heritage structures [12]. Regions with lower surface temperatures often correspond to areas with higher moisture and salt contents [4]. ERM can be used in conjunction with IRT to detect and quantify surface moisture. Moisture mapping using ERM provides insights into the distribution and extent of dampness across wall facades, with moist zones identified by ERM often aligning with those detected through IRT [2]. Given these techniques, heritage wall preservation often involves experts visually identifying areas with low temperature and high moisture in thermal images. This process requires substantial expertise, time, and effort. Automating the detection of moist regions in thermal imagery can significantly improve the efficiency of preservation workflows and contribute to the long-term safeguarding of cultural heritage. In this context, artificial intelligence (AI) applications have achieved significant progress across a wide range of fields, including cancer diagnosis [13], depression analysis [14], traffic control [15], environmental noise assessment [16], and the surface analysis of floors [17] and walls [18]. In the realm of cultural heritage, recent studies have begun exploring the use of artificial neural networks (ANNs) [19] and convolutional neural networks (CNNs) [20] to accelerate the processing of visible imagery. However, limited research has addressed the application of AI for thermal imaging analysis for heritage wall preservation.