Low-cost and scalable detection of sparse informal settlements using machine learning in Gcuwa, Eastern Cape, South Africa

Abstract

Informative and scalable cartography plays a pivotal role in curbing urban pollution, waste management, and mitigating environmental damage in the development of informal settlements. The contemporary capabilities of cloud computing facilitate streamlined access to comprehensive data repositories, computational infrastructure, and proficient tools that have rapidly advanced the execution of sprawl mapping procedures. This study tests the performance of four machine-learning algorithms, namely: Gradient Boost, K Nearest Neighbor [KNN], Random Forest [RF], and Support Vector Machine [SVM] with data extracted from cloud computing repositories for delineating informal settlements in Gcuwa, Eastern Cape, South Africa, using low-cost datasets. A systematic approach comprising iterative phases, encompassing data acquisition, the development of a training dataset, modeling, and evaluation was employed. The delineation process involved the extraction of both spectral and textural features from Sentinel-2 imagery. The Random Forest algorithm emerged as the top performer, exhibiting the highest levels of accuracy and F1 score, followed by the gradient boosting, support vector machine, and then the K-nearest neighbor algorithms. Consequently, this innovative use of machine learning algorithms with low-cost datasets and the scalable, resilient approach for detecting informal settlements offers a promising avenue for enhancing urban planning and addressing sustainable development challenges.