Urbanization in the Global South is often characterized by the proliferation of deprived neighborhoods (frequently referred to as slums). The reduction of the proportion of people living in slums is key for inclusiveness of urban areas and development and is specifically targeted by policies such as the SDG goal 11, which aims to “make cities and human settlements inclusive, safe, resilient and sustainable”. Consistent global information about the amount and spatial distribution of slums across cities in the Global South is needed to inform these policies and track progress. Unfortunately, such efforts are hampered by lacking, inaccessible, or outdated data. There is also often conceptual ambiguity of what is understood as a slum, informal settlement, or deprived neighborhood. There is a wide diversity in their appearance and perception within a single city, as well as at a global scale. To address this, we use the generic slum ontology (GSO)  and available spatial data to seek whether robust and transferable indicators with regional characteristics can be identified for global slum mapping efforts. The initial results of our analysis demonstrate that indicators such as building density and road characteristics in an image are potentially useful to describe differences between slum and non-slum built-up areas in the case-studies. This study highlights the opportunities of the GSO for the development of a global slum repository but also show the need of local adaptations and hence, the importance of the conceptualization of real-world features into image domain features. This understanding could be useful to upscale current algorithms. Further, we describe the gap between the geospatial data products developed in the remote sensing community and the information needed by policymakers and other user-groups. We discuss why an objective and transparent system for monitoring slums is needed to monitor global development goals as well as support local communities and NGOs. This becomes even more important in the time of global crisis like the spread of COVID-19 pandemic.
|Number of pages||5|
|Publication status||Published - 24 Aug 2020|
|Event||26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2020 - Online event|
Duration: 23 Aug 2020 → 27 Aug 2020
Conference number: 26
|Conference||26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2020|
|Period||23/08/20 → 27/08/20|