Unmanned aerial vehicles (UAV) have emerged as flexible, swift, and economical imaging systems that have proven their feasibility in urban infrastructure mapping. However, data derived from such systems are not utilized thoroughly. In this study, an object-oriented, multiresolution segmentation-based workflow is explored to automatically extract the urban features such as buildings and roads that can revolutionize the pace of existing mapping methods. This paper contemplates the automatic object-oriented-based feature extraction process on 5-cm true color ortho-rectified images and digital surface model (DSM). The data was generated using the JOUAV/CW-10 model and a Sony Camera with a 40-megapixel resolution. The segmentation procedure was implemented, defining various parameters such as scale, shape, and compactness. Here, the optimum scale, shape, and compactness parameters chosen for buildings and road segmentation are 100, 0.6, and 0.8, and 50, 0.5, and 0.9, respectively. The object-based image analysis (OBIA) results were compared to manually digitized features to assess the accuracy of the automated process. The fractal border error accuracy is calculated for urban features such as roads and buildings. The OBIA results indicated that completeness, correctness, and quality of building features were 98.2%, 97.6%, and 95.9%, respectively. Similarly, the road features' average completeness, correctness, and quality were 85.8%, 73.8%, and 68.2%, respectively, which is on the lower side due to obscuring of roads by the avenue trees. The methodology yielded promising results for urban feature extraction with substantial accuracy and can be implemented in other areas with little fine-tuning of feature extraction parameters.