## 3 Cases Studies: Unlocking the Sociodemographic Knowledge with EO-Methods
### Socio-Economic Inequalities and Deep Learning  
We stress the importance of adding area-level deprivation as compared to the household level concept of slums by UN-Habitat [13]. In recent years, many remote sensing studies [29; 41; 44; 53; 55; 66; 67] have highlighted the potential to map the physical aspect of deprivation, also referred to as morphological slums [68]. Such studies used mostly VHR imagery. There is a general agreement that machine learning methods have opened the door to include contextual information, besides spectral bands, to map deprived areas, e.g., by including textural features [27; 69]. The introduction of advanced machine learning methods, i.e., deep learning techniques, such as convolutional neural networks (CNNs) [53] and fully convolutional networks (FCNs) [67], allowed for automatically learning of spatial, textural, and morphological features of deprived areas, which have been previously handcrafted in more classical methods (e.g., random forest or support vector machine classifiers). However, most studies do not fully capture the complexity and variation of deprivation; they stay typically with a binary mapping output, which is resulting in high uncertainties along boundaries and for areas that are atypical slums [30; 70], e.g., deprived areas of lower densities more regular outlines but still being deprived in terms of living conditions.  
To deal with these problems, [25] introduced a data-driven approach to summarise multiple deprivation variables built on the asset vulnerability framework [71]. The socio-economic conditions at the city scale are modelled through a \"data-driven index of multiple depriations\" (DIMD). A deep transfer learning approach based on a CNN is used to  
Figure 9: Building footprints in deprived areas in Nairobi using WorldView-3 and deep learning: WorldView-3 image (**upper** row), reference building footprints using visual interpretation (**centre** row), extracted buildings footprints using U-Net (**lower** row).  
capture the socio-economic variability. The deep network, pre-trained on a slum dataset, is tuned towards the prediction of the DIMD, addressing the associated regression problem. This framework, which allows assessing the degree of deprivation of surveyed deprived areas, is extended to model the variation of deprivation at the city scale (Figure 10 shows the example of Bangalore, India). The approach is extended using open-source data, i.e., OpenStreetMap (OSM), VIIRS night-time lights, WorldPop data, census, and data from the Demographic and Health Surveys (DHS). The results show that deep learning combined with open data allows not only to map the location, extent, and physical characteristics of deprived areas but also the variations of socio-economic conditions at the city scale. This allows departing from the problems of defining the boundaries between deprived and non-deprived areas and also opens a new perspective of including a more holistic understanding of deprivation at city, urban or regional scale. Such data, being built on remote sensing and open-source data, have the potential of being regularly updated, allowing a temporal analysis of socio-economic dynamics at an urban scale.  
Timely and spatially detailed information on socio-economic conditions providing city-wide information and their link to physical deprivation is an essential input for supporting urban planning and management, allowing timely interventions and impact analysis of policies. Furthermore, such information is essential for analysing vulnerabilities linked to natural hazards [72], climate change [73], serves as contextual information for health studies [13] and supports the monitoring of the progress and supporting the achievement of several SDGs, e.g., 1 and 11 [15].