## 1 Introduction  
With the unanimous adoption of the United Nations (UN) General Assembly resolution 70/1 \"Transforming our World: the 2030 Agenda for Sustainable Development\", the Member States agreed upon a framework of 17 Sustainable Development Goals (SDG) to guide societal development. The action plan, building on the experience of the Millennium Development Goals, intertwines aspirational goals with an ambitious monitoring framework that is composed of 169 parameters in order to monitor progress made in meeting the SDGs. The capacity to monitor such progress is reported to be hampered by data gaps and entangled by the lack of statistical capacity to support the monitoring framework [1; 2; 3; 4]. In this framework, a multi-level governance of information collection, reaching across intergovernmental institutions, national agencies, and civil society has evolved. The global partnership is coordinated by the UN Statistical Commission, with the mandate of implementing the indicator framework and the review of SDG and targets [3], the Inter-Agency Expert Group on SDG Indicators, the UN Statistical Division that hosts the Committee of Experts on Global Geospatial Information Management (UN-GGIM), and Non-Governmental Organizations. This latter major group of stakeholders includes the Group on Earth Observations and the Committee on Earth Observation Satellites, which work in promoting the integration of statistical, geospatial, and other big data in order to equip the SDG monitoring framework with the necessary data and making SDG reporting possible and as complete as possible. In order to map the capacity in monitoring SDG indicators, the Inter-Agency Expert Group on SDG Indicators developed a three-tier classification for the indicators. The classification criteria are based on the simultaneous availability of an internationally agreed methodology and standard to monitor the indicator, and the presence of data produced by countries covering at least half the countries and representing half of the population of a region. As a result of the 7 th Inter-Agency Expert Group on SDG Indicators meeting in spring 2018 ([[https://unstats.un.org/sdsg/iaeg-sdsg/tier-classification/](https://unstats.un.org/sdsg/iaeg-sdsg/tier-classification/)]([https://unstats.un.org/sdsg/iaeg-sdsg/tier-classification/](https://unstats.un.org/sdsg/iaeg-sdsg/tier-classification/))), 93 indicators are classified Tier I, 72 Tier II, and 62 Tier III. Alternative and innovative sources of data, especially derived from Earth Observation (EO), offer significant information, and especially data to support the SDG reporting [5; 6; 7]. Since the early XXI century, the human society is predominantly urban, as more than half of global population lives in cities [8]. In recognition of this trait of human development, the 2030 Development Agenda devoted a specific Goal to cities: SDG 11, which aims to \"Make cities and human settlements inclusive, safe, resilient and sustainable\". Despite that human nature is so intertwined with the urban condition [9], mankind is currently able to monitor less than half of the SDG 11 indicators for its own man-made artificial environment. Many SDG 11 indicators require fine scale local data that need to be sourced locally, which often result in inadequate data availability, especially in countries in transition and in data-poor territories. EO have the capacity to make up for this information deficit, at a large scale, at high spatial resolution, repeatedly over time, and over wide geographical areas that serve multiple applications [10; 11], especially in the SDG framework [6; 12; 13] or for generic urban development indicators [14]. In this contribution, we analyze the ways in which the above support can be enacted. In the paper, we analyze the principles and the architecture of the Global Human Settlement Layer (GHSL) and the application of GHSL to the SDG 11.3.1.