부산대학교 도서관

Urbanization in the Global South is often characterized by the proliferation of deprived neighborhoods (frequently referred to as slums). The importance of improving the lives of the residents in these areas is highlighted by many global development agendas. Unfortunately, improvement efforts are hampered by lacking, inaccessible, or outdated spatial data. In this paper, we describe the current limitations which should be addressed to enable a widespread scaling up of remote sensing and image processing methodologies capable of providing this data. We focus on the conceptual ambiguity of what is understood as a slum, informal settlement, or deprived neighborhood. There is a wide diversity of their appearance within a single city, as well as at a global scale. This leads to existential and extensional uncertainty, causing even experts to have different assessments of a slum’s boundaries. Such conceptual ambiguities make it more difficult to obtain training data for image processing algorithms, as well as validation to test their accuracy. This also makes it difficult to improve the geographic, contextual, and temporal transferability of the algorithms. After discussing what is needed to upscale current algorithms, we continue to 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.