The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slum mapping-related remote sensing publications over the period of 2000–2015 regarding four dimensions: contextual factors, physical slum characteristics, data and requirements, and slum extraction methods. The review has shown the following results. First, our contextual knowledge on the diversity of slums across the globe is limited, and slum dynamics are not well captured. Second, a more systematic exploration of physical slum characteristics is required for the development of robust image-based proxies. Third, although the latest commercial sensor technologies provide image data of less than 0.5 m spatial resolution, thereby improving object recognition in slums, the complex and diverse morphology of slums makes extraction through standard methods difficult. Fourth, successful approaches show diversity in terms of extracted information levels (area or object based), implemented indicator sets (single or large sets) and methods employed (e.g., object-based image analysis (OBIA) or machine learning). In the context of a global slum inventory, texture-based methods show good robustness across cities and imagery. Machine-learning algorithms have the highest reported accuracies and allow working with large indicator sets in a computationally efficient manner, while the upscaling of pixel-level information requires further research. For local slum mapping, OBIA approaches show good capabilities of extracting both area- and object-based information. Ultimately, establishing a more systematic relationship between higher-level image elements and slum characteristics is essential to train algorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.

The continuous increase in deprived living conditions in many cities of the Global South contradicts efforts to make cities inclusive, safe, resilient, and sustainable places. Using examples of Asian, African, and Latin American cities, this study shows the scope and limits of earth observation (EO)-based mapping of deprived living conditions in support of providing consistent global information for the SDG indicator 11.1.1 “proportion of urban population living in slums, informal settlements or inadequate housing”. At the technical level, we compare several EO-based methods and imagery for mapping deprived living conditions, discussing their ability to map such areas including differences in terms of accuracy and performance at the city scale. At the operational level, we compare available municipal maps showing identified deprived areas with the spatial extent of morphological mapped areas of deprived living conditions (using EO) at the city scale, discussing the reasons for inconsistencies between municipal and EO-based maps. We provide an outlook on how EO-based mapping of deprived living conditions could contribute to a global spatial information base to support targeting of deprived living conditions in support of the SDG Goal 11.1.1 indicator, when uncertainties and ethical considerations on data provision are well addressed.

Over the last decade, Smart City has increasingly become a popular urban policy approach of cities in both the Global North and Global South. Such approaches focus on digital and technology-driven urban innovation and are often considered to be a universal solution to varied urban issues in different cities. How Smart City policies operate in contemporary cities is being examined in the emerging, but still underdeveloped, academic field ‘smart urbanism’. The considerable consequences of Smart City strategies call for critical engagement with the rationale, methods, target group and implications of Smart City approaches in different urban contexts. The aim of this paper is to further such critical engagement by distilling dimensions absent in current smart urbanism. We do so by exploring both the academic field of critical urbanism and smart urbanism and through that develop our contributions to the smart urbanism debate from existing theoretical and conceptual approaches within critical urbanism. We distilled three dimensions that require further development to facilitate a comprehensive analysis of what Smart City policies mean for contemporary urban life: (1) the acknowledgement that the urban is not confined to the administrative boundaries of a city; (2) the importance of local social-economic, cultural-political and environmental contingencies in analysing the development, implementation and effects of Smart City policies; and (3) the social-political construction of both the urban problems Smart City policies aim to solve and the considered solutions. As such, we argue that there is a lack of consideration for ‘the urbanism’ in smart urbanism.

Progress towards the UN Sustainable Development Goals (SDGs) is being evaluated through the use of indicators. Despite the importance of these indicators, the academic community has done little in terms of a critical reflection on their choice, relevance, framing and operationalization. This holds for many SDG domains, also for the urban sector domain of target 11. To partially address this void, we aim to critically review the UN methodology for the urban access indicator, SDG indicator 11.2. In discussing its conceptual framing against the background of paradigm shifts in transportation planning, we argue that this indicator has a number of shortcomings. The most important one is that it is supply oriented and measures access to transportation infrastructure, rather than accessibility to activity locations. As an alternative, we develop two accessibility indicators that show substantial variation in accessibility across geographical areas. We implement all indicators for the city of Bogotá in Colombia, using a geo-information based approach. Our results show that SDG indicator 11.2 fails to represent the transport reality well. Its supply oriented focus neglects transport demand, oversimplifies the transport system and hides existing inequalities. Moreover, it does not provide useful evidence for targeting new interventions. The proposed accessibility indicators provide a more diverse, complete and realistic picture of the performance of the transport system. These indicators also capture the large spatial and socio-economic inequalities and can help to target improvements in urban transportation.

Different types of spatial knowledge (expert, sectoral, tacit and community) are strategic resources in urban planning and management. Participatory spatial knowledge management is a major method for eliciting various types of knowledge, providing a platform for knowledge integration and informing local action and public policy. Knowledge types linked to a specific geographical locality can be integrated through geographical information systems. Recent developments in geographical information and communication technology (geoICT) have extended the opportunities for participatory spatial knowledge production, use and exchange. However, data reliability of user-generated content, social exclusion due to dependence on technology and the interpretation and implications of digital maps are major concerns. The challenge is how to integrate and utilize multiple knowledge sources for improving urban management and governance. This paper integrates the literature on knowledge types and knowledge production processes with available geoICT tools for the production, use and exchange of knowledge sources and applies it to examples from Asia, Africa and Latin America. From this review, we provide a heuristic framework for assessing the extent to which participatory spatial knowledge management tools can be instrumental on several fronts. We argue that technological developments of knowledge production have not fully addressed important issues related to accountability, empowerment, control and use of knowledge. Moreover, these developments may foster social exclusion, which could detract from the benefits of participatory spatial knowledge management in the context of urban sustainability.