## 1 Introduction
A key challenge in urban areas is the identification of disadvantaged population locations to strengthen social programs and to promote social equity [10]. However, even before COVID-19, the corresponding data were often not available or were outdated. For instance, traditional poverty measures (e.g., based on household surveys) are available at low frequency (at best every 5 to 10+ years). Due to their sampling scheme, they are only representative of coarse spatial granularity. Additionally, for many areas, they are not available; for example, between 2002 and 2011, 57 countries out of 155 (countries covered by the World Bank poverty data), had less than two data points, which is the minimum required for properly measuring poverty during a ten-year period [10]. Furthermore, data are often aggregated at geographically arbitrarty units that average and hide poor areas. Data innovations are key in support of evidence-based decision-making and to monitor the implementation of policies [10; 11; 12]. On a global scale, the causes of data gaps about slums are summarised in Figure 1. They relate to the difficulties to include temporary and mobile population (e.g., migrant workers), institutional or legal exclusion of populations without formal registration, political or operational exclusions (e.g., because of unsolved migrant status or the systematic exclusion of women or ethnic minorities in some countries), distrust of surveyed populations towards government officials (e.g., fear of eviction), low temporal granularity of censuses, limited access by data collectors (e.g., due to insecurity or simply not having records of new settlements in remote peri-urban areas), complex housing conditions that are not fully recorded (e.g., multiple occupancies or sub-renting) and general differences between the household (HH) level slum definition and an area-based understanding of deprivation [13]. For many of these issues data gaps, EO could be of great utility, offering spatially detailed data with high temporal granularity. The EO industry is undeniably exploding because EO presents an opportunity to build consistent time series that can fill data gaps. Furthermore, the availability of free imagery from satellite constellations like Landsat, Sentinel, or MODIS has democratised access to global and timely satellite imagery. Meanwhile, cloud computing providers like Amazon Web Services (AWS) and Google Cloud store satellite data for free, which is further accelerating the global usage of these EO data. Diverse applications of EO data have been linked to various dimensions of global agendas. For example, [17] listed cases related to urban development (land use, housing, transportation, water, air quality, energy, climate change and others) that can be monitored using EO and that are relevant to different global frameworks (Sustainable Development Goals (SDGs), New Urban Agenda (NUA), Sendai Framework and Paris Agreement). Users can access daily high-resolution satellite images of the entire Earth, allowing the rapid production of land cover and land use information. High- and very-high-resolution images allow the automated extraction of specific objects (e.g., buildings, cars, waste piles). Such object-level information can be used to approximate socio-economic conditions of an area (e.g., absence of cars as a poverty indicator). Additional information, for example, the presence of night-time lights (captured by night-time images), serve as an indicator of economic activities. However, these alternative methods to produce socio-economic and demographic estimates in data-scarce environments present several challenges related to the spatial coverage (e.g., limited access to high-cost very-high-resolution (VHR) imagery), resolution (e.g., the coarse resolution of night-time light imagery), comparability (e.g., combining different sensors for multi-temporal analysis)  
Figure 1: Summary of causes of data gaps in official statistics on socio-economic conditions and slums in particular (summarised from [14; 15; 16]).