## 1 Introduction  
By 2050, an estimated 68% of the world's population will reside in urban areas [1]. Urbanization concentrates exposure to disaster risk and increases socioeconomic inequalities due to unregulated sprawling development into hazardous areas [2, 3, 4]. These inequalities are further perpetuated by a lack of data relating to the exposure and vulnerability to disaster events and reported losses following disasters in lower-income countries [5], which also impedes progress towards United Nations Sustainable Development Goals (SDGs) [6]. Earth Observation (EO) satellite capabilities increasingly support progress towards sustainable development using programs of global data collection, such as Landsat, that span over 50 years [7]. EO data are now embedded in the processes of understanding natural hazards and their interactions [8], measuring and monitoring urban growth and intersection with hazards [9], informing disaster risk reduction strategies [10, 11], and responding to disaster events [12, 13]. Disaster risk reduction strategies require an understanding of the exposure and vulnerability of people and assets to hazards [14]. For example, details of building structure including age, construction method and material, and height, are key information for modeling disaster risk in seismically active regions [4; 15; 16; 17]. The automatic mapping of 2D urban growth using satellite data and image classification techniques is now widespread, e.g., [18; 19]; however, retrieving the building-level 3D structure of a city generally requires expensive aerial imagery or light detection and ranging (LiDAR) surveys [20; 21] or high-resolution digital elevation models (DEMs) [22]. Recently, approaches that can derive aggregated building height maps over 30-90 m grids have been developed, though they are best suited to buildings less than ~20-30 m tall [23; 24]. Other approaches use shadow-based height estimation; however, this does not work well in densely urbanized areas with overlapping shadows [25; 26]. Nonetheless, these approaches represent a valuable mechanism to drive the transition towards 4D city mapping, where timely updates could be envisaged. Open-source mapping datasets such as OpenStreetMap are a valuable source of mapped building footprints for use in exposure datasets or post-disaster humanitarian mapping [27]; however, data are of varying completeness globally with a mapping bias towards regions of high Human Development Index [28]. Deep learning methodologies are capable of classifying building rooftop-derived footprints in high-resolution satellite imagery [29; 30], and studies have shown that building heights can be retrieved (average errors ~4->10 m) from photogrammetrically constructed digital elevation models [16; 31]. However, application of these methods is still limited both geographically and in the spatial and temporal extent of specific case studies. Cities such as Bishkek (Kyrgyzstan) and Almaty (Kazakhstan) in the Tien Shan Mountain range, Central Asia, are exposed to high seismic risk [16; 32; 33; 34] (Figure 1a). A future earthquake in Bishkek could lead to thousands of fatalities and require hundreds of millions of dollars in rebuilding costs [35; 33]. Bishkek is expanding horizontally towards active faults and vertically through high-rise developments. Therefore, modeling the impact of future earthquakes on cities near active faults requires up-to-date exposure datasets. One necessary component is details of building structures, including age, height, construction material, and resident population [15; 36; 35]. Figure 1: (**a**) Location of Bishkek in relation to adjacent active faults overlaid on hillshaded digital elevation model. Faults are from the Kyrgyz Institute of Seismology digitized by the Active Tectonics, Quantitative Structural Geology and Geomorphology Research Group [37]. Anticlines are from Styron [38].