was at that time and the data available to allow the population of the indicators: Tier I (well-established methodology and data are widely available), Tier II (well-established methodology but data are not collected regularly by the countries), and Tier III (no established methodology to collect the required data and data are not available). Each of the SDG indicators has a set of metadata guidelines to aid interpretation and transferability; these include indicator definition (objective and purpose), computational methodology (refers to how the indicator is computed and disaggregated into multiple sub-indicators) and sourcing of data (the main source of data, collecting method, the frequency of data collection, data providers and data availability) [4]. The IAEG-SDGs regularly reviews the Global Indicator Framework to add more indicators, alter existing ones and, if required, update the status of the tier categorisation of the indicator (see Table 1). To populate the IAEG-SDG indicators at the country level, many different types of data are required; these are mostly derived from eight sources: census data (CD), household surveys (HS), agricultural survey (AS), administrative data (AD), Civil registration (CR), economic statistics (ES), geospatial data (GD), and other environmental data (Env) (Table 2) [6]. However, data alone are not enough, as they must be transformed (e.g., data combined to generate a single indicator), analysed, interpreted, and communicated to those who would make use of the indicators. Therefore, the UN noted a requirement to _\"intensify efforts to strengthen statistical capacities of developing countries and least developed countries are among the particular cases that need special attention in this regard\"_ ([2], paragraph 74). This includes a requirement to ensure access to quality and disaggregated data, including geospatial and earth observation (EO)-derived data [2]. Environmental data can include in situ observations, ground-based or aircraft remote sensing or satellite remote sensing. Here we use the broad term earth observation (EO) to describe predominantly satellite-based remote sensing. This term often also includes remote sensing from both crewed and autonomous aircraft where similar imaging techniques are used. Geospatial data play a crucial role for many of the SDG indicators, as well as for disaggregated analysis of socioeconomic SDG indicators [6; 7; 8; 9; 10]. For instance, converting household surveys into geospatial data can facilitate disaggregation and analysis by spatial characteristics, e.g., proximity to roads or levels of urban development [6]. In recent years, there have been efforts to integrate the geospatial approach into the national statistics offices (NSOs) of many countries [11]. Likewise, geospatial tools can easily manipulate large datasets such as those derived from EO satellites, aerial photography (AP), lidar, information and communications technologies (ICT) (e.g., social media, mobile, and crowdsourcing data). The processing to derive information products from these different sources is increasingly dependent on artificial intelligence and machine learning. These tools can manipulate large data sets and the artificial intelligence methods are often referred to collectively as 'big data' or the 'data revolution' [6; 7; 8]. Processing and analysing large datasets \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date of SDG Indicator Framework Update** & **Number of Indicators** & **Indicator Amendments** & **Indicator Tier Classification** \\ \hline \hline \multirow{3}{*}{\
\begin{tabular}{c} March 2016 (original framework) \\ \end{tabular} } & \multirow{3}{*}{232} & \multirow{3}{*}{N/A} & 93 Tier I \\  & & & 66 Tier II \\  & & & 68 Tier III \\  & & & 5 multiple tiers \\ \hline \hline \multirow{4}{*}{
\begin{tabular}{c} December 2020 \\ \end{tabular} } & \multirow{3}{*}{231} & \multirow{3}{*}{No modifications} & \multirow{3}{*}{No modifications} \\  & & & \\ \end{tabular}
\end{table}