Résumé : Nowadays, in sub-Saharan Africa (SSA), about 40% of the population is urban and this region is expected to face the highest growth rates during the next decades. By 2100, the three most populated cities in the world will be located in SSA. As a consequence of the extremely fast transformations experienced during the last decades, SSA cities are facing social and environmental issues combined with a lack of financial means and capacity in urban planning and management. The poorest often constitute a large part of the urban population that is extremely vulnerable to health and disaster risks.In SSA cities, up-to-date and spatially detailed geographic information is often missing. This lack of information is an important issue for many scientific studies focusing on different urban issues and there is a real need to improve the availability of geoinformation for these cities in order to support urban planning, urban management, environment monitoring, epidemiology or risk assessment, etc. . . The work presented in this thesis aims to develop different frameworks for the production of geoinformation. For this purpose, advantage is taken of Very-High Resolution Remote Sensing imagery (0.5 meters) and open-source software. These frameworks implement cutting-edge methods and can handle a large amount of data in a semi-automated fashion to produce maps covering very large areas of interest. In the spirit of open science, the processing chains are entirely based on open-source software and are released publicly in open-access for any interested researchers, in order to make the methods developed completely transparent and in order to contribute to the creation of a pool of common tools and scientific knowledge. These frameworks are used to produce very detailed land-cover and land-use maps that provide essential information such as the built-up density, or the fact that a neighborhood is residential or not. This detailed geoinformation is then used as indicators of presence of populated places to improve existing population models at the intra-urban level.