Land use change models enable the exploration of the drivers and consequences of land use dynamics. A broad array of modeling approaches are available and each type has certain advantages and disadvantages depending on the objective of the research. This paper presents an approach combining cellular automata (CA) model and support vector machines (SVMs) for modeling urban land use change in Wallonia (Belgium) between 2000 and 2010. The main objective of this study is to compare the accuracy of allocating new land use transitions based on CA-SVMs approach with conventional coupled logistic regression method (logit) and CA (CA-logit). Both approaches are used to calibrate the CA transition rules. Various geophysical and proximity factors are considered as urban expansion driving forces. Relative operating characteristic and a fuzzy map comparison are employed to evaluate the performance of the model. The evaluation processes highlight that the allocation ability of CA-SVMs slightly outperforms CA-logit approach. The result also reveals that the major urban expansion determinant is urban road infrastructure.

By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning.

Upgrading all slums in Lagos by 2030 will an ambitious task, given that more than 70% of its residents resides in slums. Furthermore, there is no recent study identifying neither the slums nor their temporal growth/development pattern in Lagos that can backstop any slum management initiative. This study aims to contribute by applying object-based image analysis and intensity analysis to map and link patterns and processes of slum growth in Lagos. RapidEye imagery from 2009 and 2015 were used to create maps for each time point for six land use categories (water, vegetated area, open space, road, slum, and other urban). Intensity analysis was applied to quantify the annual intensity of changes at the category and transition level. An overall accuracy (and kappa coefficient) of 94% (0.9) and 89% (0.86) were achieved for the 2009 and 2015 land use and land cover maps, respectively. This study showed that slums in Lagos have increased spatially during the time interval studied, with a total net gain of 9.18 square kilometers, influenced by the increase in population, mainly due to in-migration to Lagos. However, this study also revealed that slums were actively losing and gaining land area between 2009 and 2015, with an annual gain and loss intensity of 10.08 and 6.41, respectively, compared to the uniform intensity of 3.15. The gain was due to poor maintenance of buildings and encroachment onto available spaces (water and open space), while the loss was attributed to gentrification and demolition processes. A systematic process of transition was observed between slums and other urban (and open space) areas in the interval studied, and this process was mainly influenced by the Lagos state government. This analysis is crucial for designing policy interventions to manage slum growth in Lagos.