Gridded Urban Deprivation Probability from Open Optical Imagery and Dual-Pol Sar Data

With rapid urbanization leading to the proliferation of deprived urban areas (often referred to as “slums”) in sub-Saharan Africa, there is a growing number of city dwellers living in inadequate housing conditions and being exposed to multiple hazards. In this context, Earth Observation has the potential for filling gaps in spatial data availability and thereby support evidence-based policy making. We assess the potential of free open-source software, open dual-pol SAR and optical imagery (Sentinel-l and Sentinel-2), and open global datasets for producing accurate city-scale maps of areas having morphological characteristics of deprivation. Implementing a grid-based machine learning approach, we evaluate different combinations of spectral and spatial Sentinel features, and features from global data. The results show that a high accuracy can be reached with the best combinations. Since publishing maps with hard labels (e.g., deprived vs. non-deprived areas) could raise ethical concerns or even lead to misuses, the output is provided as gridded morphological deprivation probability maps.