# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
###### Abstract
The city recently introduced a 2050 urban plan that was established in 2017 for effective natural resources management through rehabilitating wetlands and rivers and restoring deteriorating landscapes to promote sustainable urban development per the sustainable development goals (SDGs) ([[https://www.gov.ls/maseru-2050-urban-plan/](https://www.gov.ls/maseru-2050-urban-plan/)]([https://www.gov.ls/maseru-2050-urban-plan/](https://www.gov.ls/maseru-2050-urban-plan/)) accessed on 14 March 2022). Gathering evidence of urban LULC changes using traditional methods such as field surveys is time-consuming and resource-intensive [12]. This process requires the application of appropriate methods to analyze the drivers and impacts of urbanization on LULC over time. Remote sensing using both air- and space-borne sensors has provided an inexpensive, timeous, and effective way to analyze the impacts of urbanization on LULC changes over time and predict future urban growth [12; 13]. This information is lacking in most metropolitan areas, particularly in sub-Saharan Africa. This limits the realization of sustainable cities and communities, preventing most urban areas in sub-Saharan Africa from achieving economic growth in terms of zero hunger, low poverty levels and job security. Therefore, it is crucial to understand the dynamics of these growth(s) in Sub-Saharan Africa and their trajectories to support proper planning and promote regional integration. Several previous studies have analyzed the impact of urbanization on LULC and predicted urban growth using remote sensing techniques [14; 15; 16; 17]. [PERSON] and [PERSON] [15] analyzed urban growth for 31 years between 1984 and 2015 in Lagos, Nigeria. They predicted urban growth for 2050, finding massive changes in urban LULC and forecasts in built-up areas with a 25.14% (120,790 km\({}^{2}\)) increase. [PERSON] et al. [14] quantified an urban sprawl between 2001 and 2016 and projected urban growth for 2021 in the Arua Municipality, Uganda. Their results showed an increase in built-up areas from 18.2% in 2001 to 40.9% of the total area. There is growing interest in using the artificial neural network cellular automata (ANN-CA) to model spatiotemporal transitions and urban growth. This has been found to be effective for analyzing nonlinear complex LULC phenomena and avoiding the automatic acquisition of conversion rules during transitional computations [18; 19]. Using self-organization, self-learning, association, and memory, ANN is capable of simplifying the acquisition of CA model conversion rules, extracting CA conversion rules from the original training data, and eliminating subjective factors, thus improving simulation accuracy [18]. [PERSON] et al. [16] analyzed spatiotemporal changes in the LULC between 2008 and 2018 and predicted urban expansion in 2038 using the MCA in the city of Nizwa, Oman. Their results showed that the city had changed by 418.5%, and by 2038 there would be an urban growth of 37,465 ha. Furthermore, the study projected a 10% decline in agricultural land and an increase of 6% in built-up areas in 2031. Most of the previous research studies used various spatial models to estimate LULC changes with models such as the Land Transformation Model [18] and the CLUE (Conversion of Land Use and its Effects) model [19]. In the present study, we used ANN-CA in the city of Maseru, Lesotho, where no previous studies have been conducted to monitor the impact of urbanization on LULC changes and to predict future urban growth. Urbanization has a positive as well as negative impact on LULC in countries in sub-Saharan Africa, which includes Lesotho [20]. Maseru is the capital of Lesotho, where a high rate of urbanization has led to massive urban growth, with the population doubling since the early 2000s [20], mainly due to high rates of immigration from other rural and urban places in Lesotho [21; 22]. Maseru is Lesotho's most developed urban center, with high employment opportunities, public services, and infrastructure facilities [22].