Abstract. A recent development in low-cost technology such as Unmanned Aerial Vehicle (UAV) offers an easy method for collecting geospatial data. UAV plays an important role in land resource surveying, urban planning, environmental protection, pollution monitoring, disaster monitoring and other applications. It is a highly adaptable technology that is continuously changing in innovative ways to provide greater utility. Thus, this study aimed to evaluate the capability of UAV-based hyperspectral data for urban area mapping. In order to do the mapping, Artificial Neural Network (ANN), Support Vector Machine (SVM), Maximum Likelihood (ML) and Spectral Angle Mapper (SAM) were used to classify the urban area. The classifications involved seven classes: concrete, aluminium, flexible pavement, clay tile, interlocking block, tree and grass. Then, the overall accuracies obtained from ANN, SVM, ML and SAM for 0.3 m spatial resolution images were 92.33%, 85.86%, 83.41% and 46.55% with the kappa coefficient of 0.91, 0.83, 0.80 and 0.38 respectively. Thus, the classification results showed that the powerful and intelligent ANN algorithm produced the highest accuracy compared to the other three algorithms. Overall, mapping of urban area using UAV-based hyperspectral data and advanced algorithms could be the way forward in producing updated urban area maps.