Combining Local Knowledge with Object-Based Machine Learning Techniques for Extracting Informal Settlements from Very High-Resolution Satellite Data

Abstract The classification of informal settlements using very high-resolution (VHR) satellite data and expert knowledge has proven very useful for urban planning. The objective of this work was to improve the accuracy of informal settlement classification within the city of Riyadh, Saudi Arabia. The analysis incorporated the use of expert knowledge (EK). Twenty unique indicators relevant to informal settlements were identified by experts familiar with these areas, and incorporated into the image classification process. Object-based image analysis (OBIA) was then used to extract informal settlement indicators from a VHR image. These indicators were used to classify the image utilising two machine learning (ML) algorithms, random forest (RF) and support vector machine (SVM) methods. A VHR image (e.g., Worldview 3) of the city was employed. A total of 6,000 sample points were randomly generated, with 1800 used for training the VHR image. The classification process was able to clearly distinguish the formal settlement areas from informal areas, road networks, vacant blocks, shaded areas, and vegetation features. The object-based RF technique provided an overall accuracy of 96% (kappa value of 95%), while OB-SVM provided an accuracy of 95% (kappa of 91%). The results demonstrated that object-based ML methods such as RF and SVM, when combined with EK, can effectively and efficiently distinguish informal settlements from other urban features. This technique has the potential to be very useful for mapping informal settlements.