## 4 Discussion
### Building Detection and Bishkek's Expansion  
Our deep learning building detection workflow was of comparable accuracy to other studies. [PERSON] et al. [67] observed an F1 score of 0.78, compared to 0.70 in our study (Pleiades dataset--Table 2). However, their study was concerned with the total number of buildings detected for population estimation, rather than the correct spatial intersection of detections and validation data. [PERSON] et al. [30] observed an overall score (mean for detections over four cities) of 0.71 as part of the DeepGlobe Building Extraction Challenge. Similarly, [PERSON] et al. [29] achieved an F1 score of 0.70 using WorldView-3 multispectral imagery. Despite comparable DSM accuracy between Pleiades and WorldView-2 (Figure 9), the lower building detection performance for WorldView-2 data (Table 2) is potentially linked to larger data gaps (e.g., Figure 12) and greater image orthorectification errors due to the stereo viewing geometry compared to tri-stereo for Pleiades (Table S7). Tri-stereo satellite image acquisitions improves data coverage and reduces DSM uncertainty [65], which is important in cities where medium- and high-rise buildings would otherwise obscure the ground as viewed from the satellite. Due to their larger pixel size, the 12 m TanDEM-X and 9 m PlanetScope DSM were not able to resolve individual buildings (Figure 12c), and the PlanetScope DSM also featured the highest vertical error (\(\sim\)8.5 m RMSE) (Figure 7b). The TanDEM-X has been used to derive average building heights aggregated to a 90 m grid,  
Figure 11: (**a**) Elevation difference between Pleiades and WorldView-2 DSMs with building polygons overlaid. (**b**) Corresponding WorldView-2 ortho image. Indicative elevation change values are shown in brackets for selected features. e.g., [24]; whereas the vertical error in PlanetScope DSMs produced using RPCs (without ground control points) likely precludes such applications despite advantages of higher resolution and high revisit frequency. Within the Pleiades AOI, we observed a 42% (85,658) increase in buildings 2013-2021 to a total of 290,714 buildings in 2021 (~10,700/ year). Bishkek's population increased ~20% (~180,000) over the same time period (Figure 3d). [PERSON] et al. [15] derived an empirical estimate of 112,293 buildings in 2009, though over a smaller area of Bishkek. Additionally, [PERSON] et al. [33] reported a total of 77,150 buildings in Bishkek (with an undefined spatial extent) in 2005, of which the majority (75,000) were stated to be private apartment houses. Comparing the 2005 data to our 2021 Google Satellite Basemap estimate would equate to a construction of 13,300 buildings per year. Notably, within the smaller WorldView-2 AOI (Figure 1b), the increase in buildings was smaller at 11,600 (2013-2019) and 15,847 (2013-2021), or ~2000 buildings per year. Though our accuracy assessment revealed similar scores between classifications (Table 2), deep learning building extraction using high-resolution satellite imagery still presents issues, particularly regarding the  
Figure 12: Example hillshaded DSMs for Pleiades (**a**) and WorldView-2 (**b**) over central Bishkek. (**c**) Example DSM elevation profile through seven buildings shown on panels (**a**,**b**). Profile runs south to north. The building footprints are shown by an arrow and horizontal line. The horizontal line is shown at the peak elevation values for each building and does not reflect the true building height, which was not known. underestimation of closely built or adjoining buildings [67], which may deliberately be marked as a single building at the training stage [78]. The prevalence of partially complete (i.e., with a roof but unoccupied) or unoccupied buildings is also not considered. Automated workflows are required to utilize a vastly increasing dataset of high-resolution imagery and to produce dynamic city inventories. For Bishkek, seismic risk models require up-to-date exposure data, since the expansion trajectory does not appear to be slowing.