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# **1. Introduction**

Capturing the 3D characteristics of the building stock plays a key role in the development of sustainable and inclusive growth policies. On the one hand, a complete and accurate quantification of the building volume and density is essential to assess the efficiency, sustainability, and resilience of settlement development and urban growth (Mahendra and Seto, 2019; Bibri et al., 2020; The World Bank, 2023b). Making “cities and human settlements inclusive, safe, resilient and sustainable” is U.N. Sustainable Development Goal (SDG) 11. In addition, building volume and building density have a high influence on the local climate (Guo et al., 2022), carbon footprint (Sun et al., 2022), traffic, and mobility (Rode et al., 2017), or adaptability to natural hazards and climate change (UN Habitat, 2020). As such, these measures are also important for SDGs 12 (Responsible Consumption & Production) and 13 (Climate Action).

On the other hand, as population continues to grow rapidly in many developing regions of the world (UN, 2017), detailed information on the building volume available to residents is crucial for the provision of housing (Adetooto et al., 2022), preservation of livable cities (Lall et al., 2021), and promotion of economic development (Adabre et al., 2020). Furthermore, evidence on future construction needs is just as important as real estate (i.e., buildings) accounts for twothirds of global wealth (McKinsey, 2021) and two-fifths of carbon emissions (UN Habitat, 2020). Also, housing represents the largest consumption item (The World Bank, 2023a) and the lack of housing is one of the main factors, if not the main factor, of urban poverty (UN Habitat, 2020).

Today, satellite imagery and computer technology allow us to map and analyze the physical form, compactness, and built-up density of settlements in 3D based on a measurement of their horizontal extent and/or vertical extent (Angel et al., 2012; Mahtta et al., 2019; Angel et al., 2021; Blei and Angel, 2021; Jedwab et al., 2021b; Frolking et al., 2022). This is especially important for developing country municipalities that do not have the administrative capacity to generate and maintain cadasters, despite how essential these are for urban planning and development (Erba and Piumetto, 2016). Recently, Esch et al. (2022), Li et al. (2022), Pesaresi and Politis (2022) and Zhou et al. (2022) generated global 3D data of the built environment that provides insights into the worldwide patterns of built-up heights and volumes. However, despite this recent progress, there is still a lack of sufficient data and knowledge at the global scale related to three issues.

Firstly, existing 3D studies still only reflect the morphological characteristics of the built-up area in general (buildings but also roads and other paved areas). For many applications, the key information needed is precise and consistent knowledge about the building stock in 3D (e.g., for estimating economic production, population distribution, or energy consumption).

Secondly, to our knowledge, most of the globally available key figures on 3D settlement morphology are currently derived from input data or analysis kernels with coarse geometric resolutions of 500m-1000m (Frolking et al., 2022; Li et al., 2022; Zhou et al., 2022).