The presence of informal settlements within Riyadh City, in the Kingdom of Saudi Arabia (KSA), has led to various urban planning issues. The current study examines these settlements using object-based machine learning (ML) methods (Random Forest (RF) and Support Vector Machine (SVM)), expert knowledge (EK) and very high resolution (VHR) WorldView-3 imagery. The study objective was to improve current mapping and planning techniques by incorporating EK in the identification process. Unique informal settlement indicators identified by the urban specialists were used in a combined ML and image analysis (OBIA) approach. Results show that combining ML, EK and remotely-sensed data in the analytical work can efficiently, effectively and accurately distinguish unplanned areas from other areas. This provides a very useful method for mapping informal settlements.