The goal of this work is to present a web application that uses machine learning methods to aid travelers in recognising Indian monuments. The process of digitizing this cultural heritage preservation involves collecting large datasets of images, cleaning the data, and training a model based on Indian Monuments using Pytorch. The model was trained using MobileNet V1 architecture for monument prediction and MobileNet V2 for satellite identification. By deploying this model into a Python Flask Web application, users can capture an image of a monument and receive information about it, such as nearby tourist spots, hotels, user ratings, price range, reviews, a description of the monument, official website, and a link to a 360-degree view. The results showed that MobileNet V1 achieved an accuracy of 97% for monument identification, and MobileNet V2 achieved 93.3% for satellite identification. Applying data augmentation to the latter model resulted in an accuracy of 95%. This paper offers a solution to the lack of proper entry and tourism facilities for less familiar monuments and the shortage of trained guides. It also provides an interactive learning process that can stimulate users' interest in cultural heritage.

Indian Monuments, MobileNet , Satellite view, Web Application