## 4 Assessing EO Contribution to SDGs Indicators Using a Maturity Matrix Framework (MMF) Approach
A weak contribution of EO data to supporting the indicators occurs primarily among indicators within the more socio-economic category. By its nature, SDG indicator 8.3.1 (proportion of informal employment in total employment, by sector and sex) cannot be supported directly by EO data; nevertheless, [PERSON] et al. [179] explored the potential for estimating the formal and informal economy for Mexico using known relationships between the spatial patterns of nighttime satellite imagery and economic activity. They developed regression models between spatial patterns of nighttime imagery and adjusted official gross state product (AGSP) for the USA states. These regression parameters derived from the regression models of the USA were 'blindly' applied to Mexico to estimate the estimated gross state income (EGSI) at the sub-national level and the estimated gross domestic income (EGDI) at the national level. Comparison of the EGDI estimate of Mexico against the official gross national income (GNI) estimates suggested that the magnitude of Mexico's informal economy and the inflow of remittances are 150% larger than their existing official estimates in the GNI. Comparison of the MMF results from 2019 to 2021 (see Supplementary Material Figures S1 and S2) reveals 30 new indicators over these approx. 2 years where there is published evidence that EO can provide some support. This suggests that researchers are increasingly finding ways to use EO data in social and economic indicators. It also reflects the amendments agreed by IAEG-SDG in December 2020 that introduced new indicators and revised or removed others. Figure 4 illustrates these 30 indicators with new EO-based methods, categorised into their type and role of the EO contributions. Many of the social indicators that have new EO-based methods are related to education. For instance, in one of the new approaches, [PERSON] et al. [180] noted that EO data can provide appropriate reliability, accuracy, and convenience for identifying rural schools in Liberia via machine learning approaches; based on their approach, UNICEF [181] launched an initiative to map every school in the world, thus contributing to achieving SDG 4 (quality education). In addition, the usage of night-time satellite data can help monitor aspects of education facilities, such as access to electricity [182; 183] and drinking water [184], which are closely linked to SDG 4.a.1 indicator (schools with basic service as electricity and internet). Furthermore, [PERSON] et al. [185] used VHR satellite data and enrolment data to calculate the primary school classroom area per pupil in rural Nigeria. They found that over 70% of the 1900 schools evaluated could be classified as overcrowded (according to Nigerian government metrics), this being highly correlated with poverty and literacy rates. [PERSON] et al. [185] also analysed their area per pupil result via the MMF approach, which characterised this EO support as partial for the SDG indicators 4.1.2. (Completion rate in primary education, lower secondary education, upper secondary education) and 4.6.1 (Proportion of population in each age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sex). While area per pupil is not an SDG indicator, it potentially provides a useful contribution since overcrowded schools influence SDG indicators, such as completion of primary education and absenteeism, and can lead to low literacy and  
numeracy rates. The study is a proof of concept that was further validated in situ by [PERSON] et al. (2016). The good health and well-being goal (SDG 3) is another social goal where new methods have been derived to evaluate indicators using EO data ([PERSON] et al., 2016; [PERSON] and [PERSON], 2017; [PERSON] et al., 2018). For example, [PERSON] et al. (2017) used EO images and machine learning to assess health services in remote villages, thus providing information that partially contributes to indicator 3.8.1 (coverage of essential health services).