## 3 Cases Studies: Unlocking the Sociodemographic Knowledge with EO-Methods
### The Role of Low-Cost Data for Mapping Slums  
The majority of studies on mapping deprived urban areas (e.g., slums) work with commercial VHR imagery. Analysing all studies from recent years found in Scopus (N = 28) [15; 24; 25; 28; 29; 30; 36; 41; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63], more than 50% work with VHR optical imagery, followed by 30% high resolution (HR) optical (e.g., Sentinel-2) or Google Earth imagery and bit less than 20% with SAR imagery (both VHR and HR). The dominance of VHR imagery is also contributing to the dilemma of most studies developing and testing methods on very small areas (usually few km\({}^{2}\)), which is insufficient to reflect on the potential of integrating EO and statistical data, both typically available for large area coverage.  
In general, within the EO community, there is an understanding that VHR imagery is necessary to reveal complex urban patterns and socio-economic information [21]. To understand the relation between image cost and resolution, a literature review was carried out on slum mapping studies published (including preprints) between 2017-2020. In the retrieved literature (N = 28), the relationship between accuracies and EO data costs was analysed (Figure 7). The image costs were grouped into three classes (where \"High\" means more than 10 EUR/km\({}^{2}\), \"Moderate\" meaning 10 EUR/km\({}^{2}\) or less, and \"Free\" imagery without costs), there is no strong relation between high accuracies and high imagery cost. The variations within the three classes (of image cost) can be explained by the use of different machine learning algorithms and the different complexity of the cities. In general, free imagery (mainly Sentinel and Google Earth) achieved a bit lower accuracy, but reached an accuracy of 80% on average. Thus, they are a promising alternative to avoid image data costs. As an example, in the SLUMAP project ([[https://slumap.ulb.be/](https://slumap.ulb.be/)]([https://slumap.ulb.be/](https://slumap.ulb.be/)), accessed on 10 June 2021), gridded maps were produced using Sentinel-1/2 (Figure 8).  
Several studies (e.g., [63; 64; 65]) have confirmed that VHR resolution images (of 1 m and below spatial resolution) are not optimal for mapping deprived areas at city scale as they are impacted by too-high-level details, which is adding noise to the classification. Thus, an optimal resolution for city-scale maps depends on the urban morphology and typically vary around 2-5 m to capture deprived areas. However, this conclusion differs when it comes to mapping objects (e.g., buildings). For this purpose, VHR imagery is fundamental to map building objects that are required for many urban morphological analysis purposes. A resolution definitely below 0.5 m is required to map buildings in deprived areas. However, in very densely built-up urban areas, such as deprived areas in Nairobi (shown in Figure 9), even 0.3 m is not sufficient to capture all roofs as separate objects (lower row Figure 8). Roofs in such areas are forming larger blobs which, even for visual interpreters, are difficult or even impossible to separate.  
Figure 8: Gridded land-cover/land-use map (50 m \(\times\) 50 m) of Nairobi highlighting deprived areas, produced from Sentinel-1 (SAR) and Sentinel-2 (optical) image features using a random forest classifier.  
Figure 7: Comparing classification accuracies and image data costs (high \(>\) 10 EUR/km\({}^{2}\), moderate \(\leq\) 10 EUR/km\({}^{2}\) and free data) (number reviewed literature N = 28).