## 5 Conclusions  
In this article, we provide a concrete example of the contribution of Earth Observation and innovative geospatially derived data in support of the Sustainable Development Goals. The SDGs were negotiated in parallel to a comprehensive and ambitious monitoring framework. This framework is currently hampered by shortcomings in data availability and the statistical capacity for monitoring it, with only 93 indicators with a Tier I classification. Earth Observations set out as a key source of information to service SDG monitoring: by improving the availability of required data, supplying suitable and accurate data that cover long time series, and having wide/planetary geographical scope are compatible/complimentary with traditional statistical methods. To provide a practical application of this service, we estimated, at the global level, Land Use Efficiency (SDG indicator 11.3.1), which is currently classified as a Tier II indicator due to the presence of an internationally agreed methodology, but in the absence of data. The GHSL provides the necessary data, harmonized in space and time, on the spatial distribution of built-up areas (GHS-BUILT), population (GHS-POP), and settlement typologies (GHS-SMOD), fulfilling the UNDESA metadata requirements for the indicator. In addition to the above, we also discussed the principles and architecture of the Global Human Settlement Layer that by design embraces an open input, open method, open output policy, has a modular hierarchical structure of information, tests and applies real-word (big) data scenarios, produces evidence based output analytics, and facilitates the repeatability of results. In particular, we emphasize the role of multi-temporal and spatial harmonization of information to enable comparative studies and analyses on development trajectories. The quantitative results of the experimental application of GHSL data in support of the SDG indicator 11.3.1 show that urban centers developed between 1990 and 2015 with an LUE that was equivalent to 0.72, whereas the rate of population growth has been greater than that of spatial expansion. In absolute terms, urban centers expanded over the terrestrial land surface to almost 67,800 km\({}^{2}\) (approximately the area of Ireland) to settle almost 1.1 billion new people (almost the population of India in 2015). To further characterize LUE, we compared the built-up areas per capita in the epoch 2015 achieved in centers that developed in common LUE classes in various regions of the world. It emerges that there might be inequalities in the trajectories of development of centers that develop with comparable LUE values, but are located in different geographical areas. This is perceived, in particular, in comparing the changes in built-up areas per capita between 1990 and 2015. With the estimate of the LUE, we enacted UNGGIM instruction to test GHSL for the purpose. In this context, we reaffirm that, while GHSL data could support data-poor territories, they could also back data-rich parties for harmonization and comparison purposes. Our research concludes that the GHSL framework, with its tools and data, enables users to generate built-up area layers that can be used directly with UNDESA compiled demographic data to quantify LUE. New satellites (Sentinel constellation) are sources of information to map built-up areas for recent years and for the future with a higher resolution when compared to the open and free platforms of the past. The workflows that were generated in the open and free GHSL environment to map built-up areas, including the MASADA tool, could support the estimation of the Land Consumption Rate to 2030 with open data and open tools.