This project aims to develop a computer vision system capable of detecting and classifying domestic geographic landmarks in images and video content. By categorizing locations such as “childcare centre”, “school”, “shopping centre”, “hotel”, “bus stop”, “train station”, and so on, the system provides a reliable way to identify key landmarks in urban and suburban environments. Using advanced machine learning techniques, the model processes visual data to recognize distinct architectural features, signage, and contextual cues associated with each landmark type. It enhances location-based services by allowing for precise geographic categorization of visual content, enabling improved search and retrieval of place-specific information. Additionally, it supports applications in safety, law enforcement, tourism, and transportation by automatically identifying and categorizing critical public spaces.