## 4 Discussion
### Open Data and Tools Filling Gaps of a Tier II Indicator
The tool is designed to work with GHS-BUILT and GHS-POP, but other spatial and demographic layers could also be used. With this tool, the user is free to use proprietary data on built-up and population and to set ad hoc population criteria and threshold to define settlement typologies. The LUE Tool (a Qgis plugin) [63] enables the estimation of the SDG 11.3.1 in GIS environment with an input of spatial multi-temporal information on the spatial extent of the settlement and its demography at the corresponding epochs. While this study proposes a comprehensive retrospective estimation of the LUE indicator across the globe, it is challenged by the input data being used. * The definition of land consumption according to the GHSL data: in the GHSL concept and datasets, built-up areas that correspond to all man-made roofed constructions are used as proxies to Land Consumption. The latter, according to the UN definition, may be extended to cover other man-made features, such as roads, parking lots, or other artificial and impervious surfaces (and more closely align to SDG15.3.1);
* despite the use of increased spatial resolutions for built-up areas detection (as compared to precedent EO derived urban maps), some settlements can still be omitted due to their size or construction materials and some false detections may still be observed, especially over rocky bare lands; and,
* moreover, because the method for mapping built-up areas is based on physically observable built-up structures, as collected from satellite-borne sensors, some settlements could not be detected. Examples of invisible settlements as from the satellite remote sensing sensors that were adopted in this study include: small built-up structures below dense tree canopy, settlements carved in rock cliffs or underground, scattered huts in rural areas built with traditional materials, such as straw or clay (not distinguishable from the background soil and vegetation patterns), and some temporary settlements, such as tent refugee camps. In that context, the validation of built-up areas is essential for achieving a comprehensive and systematic description of the quality and validity of the GHSL built-up areas product. Continuous activities are performed in that direction in order to provide quality metrics that may allow for them to model, retro-fit, and compare the results that were obtained from the different GHSL products. Despite these caveats, the GHSL suite of data and tools encompasses a wide spectrum of functionalities to support the SDG monitoring, both within its own resources (especially data), but also by offering free tools to users, empowering them to use third party or proprietary information sources, especially in the context of SDG 11.3.1. In particular, the GHSL architecture retains potential for the estimation of LUE to 2030. With the entry into service of the Sentinel-1 and Sentinel-2 constellations and the conception of workflows to process their data for built-up areas extraction in GHSL environment [64; 65] it is to foresee a community-based capacity to periodically update LCR estimates for the entire Earth.