Many cities in Low-to-Middle-Income Countries are facing unplanned and rapid growth of built-up areas and lack detailed and
disaggregated information to address this challenge. Most unplanned urban growth results in deprived areas defined by slum-like
conditions, informality, and high poverty rates. Such deprived areas are heterogeneous: they present differences in socioeconomic status,
and visible differences in housing conditions and urban spaces. The extent to which such physical differences are linked to urban
deprivation is still unknown, while this topic is at the heart of SGD 11 aiming at making cities and human settlements inclusive,
safe, resilient, and sustainable. The general objective of this project is to showcase the contribution of EO combined with
Citizen Science to categorize urban deprivation. Working in one of the most challenging topics in urban environments, that of combining
local perception of residents with what is shown from satellite imagery, PARTIMAP will unravel these complex relationships into
hybrid deprivation models that do not suffer from large levels of bias. The results of workshops involving communities living in
deprived areas (so called 'slums') of Nairobi, Kenya, will be analysed, evaluated, and combined with EO-based outputs of the SLUMAP
project (very-high-resolution satellite derived land-cover/land-use maps, morphological indicators) into a characterized hybrid
urban deprivation model of the city.