Routine and accurate data on deprivation are needed for urban planning and decision support at various scales (i.e., from community to international). However, analyzing information requirements of diverse users on urban deprivation, we found that data are often not available or inaccessible. To bridge this data gap, Earth Observation (EO) data can support access to frequently updated spatial information. However, a user-centered approach is urgently required for the production of EO-based mapping products. Combining an online survey and several forms of user interactions, we defined five system specifications (derived from user requirements) for designing an open-access spatial information system for deprived urban areas. First, gridded maps represent the optimal spatial granularity to deal with high uncertainties of boundaries of deprived areas and to protect privacy. Second, a high temporal granularity of 1–2 years is important to respond to the high spatial dynamics of urban areas. Third, detailed local-scale information should be part of a city-to-global information system. Fourth, both aspects, community assets and risks, need to be part of an information system, and such data need to be combined with local community-based information. Fifth, in particular, civil society and government users should have fair access to data that bridges the digital barriers. A data ecosystem on urban deprivation meeting these requirements will be able to support community-level action for improving living conditions in deprived areas, local science-based policymaking, and tracking progress towards global targets such as the SDGs.

Spatial data on Low-and-Middle-Income-Country (LMIC) cities, and deprived areas within cities, are often not readily available in support of local and global information needs. To address this information gap, we propose the systematic semi-automated SLUMAP framework that provides policy-relevant information on deprived urban areas in Sub-Saharan Africa (SSA), based on free open-source software (FOSS). First, we assess user needs for spatial information on deprivation (ranging from local communities to global research and policy support). Second, we show how free or low-cost image datasets can be used for mapping the location of deprived areas at the city scale and providing an overall assessment of their spatial patterns. This is implemented as a grid-based approach using machine learning and assessing the contribution of a large number of spectral and spatial features derived from open or low-cost imagery. Third, we show how higher (spatial and spectral) resolution data can provide a detailed characterization of such areas, with a GEOBIA/machine-learning workflow and deep learning techniques. We illustrate the experiments and results on the city of Nairobi (Kenya)and discuss transferability to SSA cities.