AI Image-based Systems for Enhancing the Cultural Tourism Experience

Technological innovation, conservation and enhancement are key elements for promoting cultural heritage and attracting visitors worldwide. Cultural tourism represents a significant economic and social strategy, capable of stimulating regional development and revitalizing marginalized areas. In recent years, artificial intelligence (AI) has transformed the sector, offering new ways to engage with and preserve cultural assets. This study explores the application of advanced AI techniques, particularly Convolutional Neural Networks (CNNs), to improve the classification and recognition of images related to architectural heritage. A deep learning algorithm specifically designed for cultural heritage enhancement is presented, focusing on the automatic classification of images of buildings and monuments. The analysis includes a comparison between pre-trained models and custom models, highlighting the performance of different approaches. The experimental results demonstrate that our ensemble model achieved 90% accuracy in classifying architectural heritage elements across 10 categories, with the Majority Vote Ensemble approach outperforming individual models by 6-10%. This improved classification accuracy enables more reliable automated systems for cultural heritage documentation and interactive tourist experiences. The research addresses key challenges in architectural heritage classification including variations in preservation state, lighting conditions, and complex backgrounds with multiple elements. The study also investigates the impact of data augmentation and class balancing techniques on model performance, demonstrating how these methods can mitigate limitations in training data availability. The developed ensemble combines state-of-the-art CNN architectures like ResNet50 and EfficientNet with custom models, leveraging their complementary strengths to achieve robust classification across diverse architectural styles and conditions. The adopted approach is supervised, using a training dataset where images are pre-labeled according to specific categories. This allows the algorithm to learn and subsequently predict the categories of new images based on the acquired information. A practical application is proposed to improve visitor experiences and heritage management, paving the way for future technological advancements in the field.