Image-Based Destination Recognition in Smart Tourism Systems using ML (CNN)

Intelligent tourism systems have revolutionized the manner in which tourists visit and experience places. Nevertheless, conventional location-based suggestions tend to depend on GPS coordinates, which could be unreliable in densely populated or indoor settings. This paper introduces an Image-Based Destination Recognition System (IDRS) based on Convolutional Neural Networks (CNNs) for destination identification and suggestion of travel destinations using images taken by users. The system applies deep learning methods to image analysis, landmark recognition, and personalized travel recommendations. The model is pretrained on a diverse dataset of popular tourist destination images and fine-tuned for real-time inference. Benchmark tests exhibit high accuracy in destination identification, which makes IDRS a useful tool for smart tourism applications. In addition, the system improves travel experiences through the delivery of information regarding known sites, suggesting attractions nearby, and presenting historical and cultural information. The combination of CNN-based recognition and travel applications offers a painless and interactive means of having travelers explore places without the drawbacks of reliance on GPS. Experimental results show that the system has state-of-the-art accuracy and demonstrates its effectiveness in real applications, validating the development of AI-based tourism technology.