<![CDATA[The increasing number of car users in colleges has posed significant challenges in effectively managing parking lots. This research focuses on the implementation of Google Cloud Vision to detect and monitor the availability of car parking lots in a college environment. By utilizing cloud-based image processing services and machine learning algorithms from Google Cloud Vision, the proposed system aims to provide real-time analysis and reporting on parking slot availability. The methodology used involves capturing images of the parking area using strategically placed cameras, then uploading these images to Google Cloud Vision to detect the presence or absence of cars. The system is designed to be scalable, ensuring that it can handle different sizes and complexities of parking areas. Preliminary results show that the approach using Google Cloud Vision offers a high level of accuracy in identifying occupied and empty parking slots, thus providing a reliable tool for parking management in a college environment. This work discusses the development, implementation, and evaluation of the system, and highlights its potential in improving parking lot utilization efficiency and reducing the time users spend searching for available parking spaces.]]>
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