Integrating geographical information systems, remote sensing, and machine learning techniques to monitor urban expansion: an application to Luanda, Angola

Abstract

ABSTRACT According to many previous studies, application of remote sensing for the complex and heterogeneous urban environments in Sub-Saharan African countries is challenging due to the spectral confusion among features caused by diversity of construction materials. Resorting to classification based on spectral indices that are expected to better highlight features of interest and to be prone to unsupervised classification, this study aims (1) to evaluate the effectiveness of index-based classification for Land Use Land Cover (LULC) using an unsupervised machine learning algorithm Product Quantized K-means (PQk-means); and (2) to monitor the urban expansion of Luanda, the capital city of Angola in a Logistic Regression Model (LRM). Comparison with state-of-the-art algorithms shows that unsupervised classification by means of spectral indices is effective for the study area and can be used for further studies. The built-up area of Luanda has increased from 94.5 km2 in 2000 to 198.3 km2 in 2008 and to 468.4 km2 in 2018, mainly driven by the proximity to the already established residential areas and to the main roads as confirmed by the logistic regression analysis. The generated probability maps show high probability of urban growth in the areas where government had defined housing programs.