This present study explores the potential of utilizing Unmanned Aerial Vehicle (UAV) data for mapping urban areas, emphasizing the effectiveness of combining UAV technology with Object-Based Image Analysis (OBIA) in updating maps. In dynamic urban environments where changes occur frequently, this combination provides a rapid and efficient method for map updates. The study's primary objective was to extract valuable information from UAV data using OBIA. The research methodology involved capturing UAV images, followed by photogrammetric processing to generate orthophoto, Digital Surface Model (DSM), and Digital Terrain Model (DTM). Subsequently, OBIA was employed to classify the image, utilizing a range of machine learning-based algorithms for image classification. A comparative analysis was conducted to evaluate the performance of different classification algorithms. It was observed that the K-Nearest Neighbour (KNN) algorithm demonstrated superior performance, outperforming all other algorithms in accurately classifying the image.