Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations’ (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land use expansion patterns and thereby informing sustainable urbanization initiatives (i.e., SDG 11). Our work enhances a UN proposed delineation method by integrating various open-source datasets and tools (e.g., OpenStreetMap and openrouteservice) and advanced geospatial analysis techniques to automate the delineation of individual functional urban agglomerations across a country and, subsequently, calculate SDG Indicator 11.3.1 and related metrics for each. We applied our automated geospatial approach to three rapidly urbanizing countries in Africa: Ethiopia, Nigeria, and South Africa, to conduct multi-level examinations of urban land use expansion, including identifying hotspots of SDG Indicator 11.3.1 where the percentage growth of urban land was greater than that of the urban population. The urban agglomerations of Ethiopia, Nigeria, and South Africa displayed a 73%, 14%, and 5% increase in developed land area from 2016 to 2020, respectively, with new urban development being of an outward type in Ethiopia and an infill type in Nigeria and South Africa. On average, Ethiopia’s urban agglomerations displayed the highest SDG Indicator 11.3.1 values across urban agglomerations, followed by those of South Africa and Nigeria, and secondary cities of interest coinciding as SDG Indicator 11.3.1 hotspots included Mekelle, Ethiopia; Benin City, Nigeria; and Polokwane, South Africa. The work presented in this study contributes to knowledge of urban land use expansion patterns in Ethiopia, Nigeria, and South Africa, and our approach demonstrates effectiveness for multi-level evaluations of urban land expansion according to SDG Indicator 11.3.1 across urbanizing countries.

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa’s anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous “developed” or “built-up” area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses.

Geospatial data play a significant role in the United Nation’s Sustainable Development Goals, particularly through assessments of monitoring indicators. Sustainable Development Goal (SDG) Indicator 11.3.1 assessments utilize land and population geospatial data to monitor urban expansion, and were implemented to enhance inclusive and sustainable urbanization, and capacity for participatory, integrated and sustainable human settlement planning and management in all countries by 2030. Data-limited countries often rely on accessible, wide-coverage geospatial datasets for SDG Indicator 11.3.1 assessments which may have characteristics (e.g., coarse spatial resolution) influential to assessment outcomes. The presented work examines the effect of land use data spatial resolution on SDG Indicator 11.3.1 assessment components including urban area delineation, SDG Indicator 11.3.1 and supporting spatial metrics, spatial patterns of urban land development, and land use change patterns for urbanizing areas in Ethiopia from 2016 to 2020. A comparison was made between a single land use map at the spatial resolution in which it was originally produced, 30 m, and at a majority-resampled spatial resolution comparable to many global coverage datasets, 90 m. Analyses revealed changes in the urban areas identified, observed boundaries of urban areas, and all quantified metrics from 30 m resolution to 90 m resolution, with the decrease in resolution resulting in smaller urban areas being missed and differences in the delineated hinterland areas connected to an urban core. Statistical testing indicated significant differences in SDG Indicator 11.3.1 values, developed land use area per capita, and spatial patterns of urban development between the two spatial resolutions. The relative order of land use conversion types remained similar across both resolutions, with agricultural land experiencing the greatest conversion to developed land, followed by rangeland and forest, although the total area of each conversion type differed. This study illustrates the possible discrepancies in SDG Indicator 11.3.1 and related outputs when utilizing differing resolution datasets and the importance of data characteristic consideration when conducting SDG Indicator 11.3.1 assessments.