# The IoT/IoE Integrated Security & Safety System of Pompeii Archeological Park
## 3 Integrated Technology Systems in Cultural Heritage
These technologies can support inspection professionals by enhancing the accuracy and reliability of damage assessments. Different techniques of image-based damage assessment techniques, with a particular emphasis on the use of deep learning methods for cultural heritage conservation, have been proposed, including several case studies where AI tools complement traditional visual inspections, helping to advance the preservation of historic structures [17]. Video surveillance can be used to realize a vision sensor to implement a dual-service security system for museums. It consists of a system that offers the following two main functions: object monitoring and invisible visual Multiple-Input Multiple-Output (MIMO) communication. For object tracking, a Kalman filter algorithm is employed to predict and monitor the position of items within the surveillance area. To enable covert visual MIMO communication, an Adaptive Row-Column (ARC)-based Least Significant Bit (LSB) substitution technique is combined with the Integer Wavelet Transform (IWT) method. Both services are realized using standard electronic visual displays and existing surveillance cameras, ensuring minimal infrastructure modification. The performance of the object tracking system was assessed by comparing the actual object positions with the estimated ones to determine accuracy. Likewise, the covert visual MIMO system's performance was evaluated at two stages as follows: imperceptibility was measured by analyzing the differences between cover and stego images on the transmitter side, while reconstruction accuracy was assessed by comparing original and decoded data images at the receiver side. The proposed system achieves enhanced imperceptibility, higher accuracy, and greater robustness compared to existing methods [18]. In recent decades, the need to protect and enhance cultural heritage structures (CHSs) has become increasingly critical, given their unique value and their wide-ranging economic, social, cultural, and environmental impacts. Structural Health Monitoring (SHM) and the effective management of CHS are emerging as essential strategies to assess the current state of conservation and structural integrity. Additionally, the data collected through SHM systems play a crucial role in developing cost-effective and sustainable maintenance plans, aligned with the core principles of historic preservation, such as minimal intervention. However, applying SHM to CHS presents greater challenges compared to modern buildings, due to the uniqueness of each historic structure and the imperative to preserve its architectural and historical authenticity. From this point of view, there are plenty of traditional and advanced SHM techniques applied to cultural heritage structures. They also include the use of fiber optic sensors, smart sensing materials, IoT-based SHM systems, and the integration of Building Information Modeling (BIM) technologies in heritage conservation [19]. Also unmanned aerial vehicles (UAVs) have been used in a cultural heritage context. For example, in utilizing deep learning techniques to detect looting activities at heritage sites using optical imagery captured by UAVs, demonstrating that UAV optical data offer an accessible and effective solution for authorities to monitor heritage sites and showcasing the potential of deep learning to automate the detection of new damage. It also demonstrates the broader implications of applying deep learning for the protection of cultural heritage and explores how the approach could be enhanced with the integration of additional data sources [20]. The use of UAVs also enables the detailed analysis of sites of cultural interest such as the study made on Cala Castle in Huelva, a key medieval fortification rehabilitated between 2003 and 2011 in Spain. A decade after its restoration, UAV-based 3D models and photogrammetric surveys have been created to document both the site and its landscape context.