The availability of high quality RGB images and LIDAR data provides efficient image classification using the complementary properties of these data sources. The paper objective is to introduce an automated urban unsupervised classification technique using combined semantic (from RGB image) and spatial (from LIDAR data) information leading to the ability to extract different features rabidly and efficiently. In this study, new concepts and techniques for mapping urban areas using aerial images and LIDAR data fusion was developed and tested based on available data. Genetic Algorithms (GAs) were integrated with different fitness functions to produce two proposed techniques. K-Means (KM) and Fuzzy C-means (FCM) algorithms were tested and compared, as fitness functions for GAs. Three groups of data were applied which include: RGB group; RGB/ LIDAR data group and RGB/LIDAR/attributes group. Error matrix and K-HAT (kappa) statistics were adopted as well as visual inspection to evaluate the classification results. FCM proved to be a preferable fitness function for GAs-based classification from aerial images and LIDAR data with accurate average classifications of 87.84%. The feature classification techniques developed in this study are automated, efficient and present a suitable method for extracting different features while overcoming most of the problems in situations of similarity existing in texture or height information accompanied by fast and reliable results. During the vectorization phase, the classified images were processed through a series of image processing techniques to produce the digital vector buildings map. An accurate estimation of buildings was carried out from the reference data and compared against the corresponding results. The extracted building data coordinates were compared against the Global Positioning System (GPS) observations and the standard deviation was 0.43m.