## 4 Discussion
### EO Derived Information on Human Settlements  
In recent times, the remote sensing and EO technology to acquire, process, and manage data has significantly improved. In particular, significant advances to the instruments to acquire information from space have taken place. The spatial resolution of new sensors has increased, as has temporal revisit time. Early EO derived products to map the extent of human settlement relied on coarse resolution imagery (MODIS or Landsat 1-3 MSS) or on a combination of the latter and nighttime lights (i.e., DMSP/OLS). Although EO-based methods of settlement information extraction have helped in expanding the scientific understanding of human presence on the planet [16; 60; 66; 67], the lack of extensive validation and training sets covering multiple epochs and diverse geographical areas may limit accuracy assessments. The GHSL data have been validated in order to assess both the built-up areas detection (GHS-BUILT) [68; 69] and the population map [70]. The results of the validation demonstrate that GHSL is one of the most reliable global, open, and free data, with the layer accuracy increasing over time and growing with development intensity. Albeit that GHS-BUILT and GHS-POP provide multi-temporal information mapping the extent and resident population of human settlements around the globe and representing a substantial improvement when compared to earlier EO derived products [46], recent satellites are expected to significantly improve this information. For example, the use of Sentinel 1 (GHS BUILT-UP Sentinel-1 Grid) as a training set for the Landsat, as implemented in [51], has enhanced the global delineation of human settlements from 40 years of Landsat satellite data archives. Recent platforms equipped high-resolution (i.e., Sentinel-2) and very-high-resolution optical (i.e., SPOT) or synthetic-aperture radar technology (i.e., TerraSAR-X and Sentinel-1) proved to be effective in delivering maps of human settlements [30; 64; 65; 71]. This new generation of information support researchers and policymakers in the understanding of the new geographies of human settlements in the era of a predominantly urban society. In particular, the finer spatial resolution of sensors contribute to the improvement of the detection of small roofed surfaces and urban features extraction [72], reducing omission and commission errors [64; 73], and improving settlement mapping in territories subject to intense and fast transformations (especially in Africa and Asia). The SDG 11.3.1 methodology requires globally comparable information to analyze the interdependence between spatial expansion of settlements and their demographic changes. Most of the existing geospatial data does not possess the characteristics that are necessary to estimate SDG 11.3.1 These are: (1) multi-temporal/diachronic information with global coverage; (2) demographic (PGR) and spatial (LCR) information; (3) harmonized urban-rural classification of settlements (to delineate areas of interests); and, (4) open and free data with open input, process, and output to fully support public policy. Our research findings support rationales on urbanization that allege that urban areas are efficient in terms of use of land resources [74], and that urban development is highly unequal across regions of the world [75]. Estimating a LUE < 1 corresponding to 25 years of urban development (1990-2015), we empirically illustrate that the rate of demographic growth in urban centers has exceeded the growth in space. Although GHS-BUILT does not account for the vertical dimension of built-up areas, this value could validate a dense/compact pattern of development that implies population densification, the diversity of LUE values across regions of the world, and the corresponding built-up areas per capita may reinforce the signals of widening urban divide, in terms of inequality in development. In particular, the comparison of built-up areas per capita in 2015 broken down into LUE classes and regions of the world manifests the traits of unfairness.