## 5 Conclusions  
In this study, we assessed the growth of Bishkek through time, including deriving a workflow to classify built-up area and areas of redevelopment in a 1979 KH-9 Hexagon satellite image. We used high-resolution stereo and tri-stereo satellite imagery to derive building polygons and their heights, which were validated using ICESat-2 altimetry data. Bishkek expanded by \(\sim\)139 km\({}^{2}\) (92%) (1979-2021), particularly towards active faults located at the south of the city and \(\sim\)26% (59 km\({}^{2}\)) of Bishkek's built-up area classified in 1979 was observed to have redeveloped by 2021. We found that building polygons extracted using a deep learning workflow applied to high-resolution tri-stereo (Pleiades) satellite imagery were most accurate (F1 score = 0.70) compared to stereo (WorldView-2) imagery (F1 score = 0.61), and that building heights extracted using a Pleiades-derived digital elevation model were most comparable to independent measurements (normalized absolute median deviation \(<\) 1 m). Across different areas of the city, our analysis suggested rates of building growth in the region of 2000-10,700 buildings per year. The horizonal and vertical expansion of Bishkek demonstrates the importance of up-to-date exposure data, which are required to produce seismic risk models. Our analysis highlighted the capabilities and limitations of using earth-observation data to update estimates of building stock, where country-level or open access datasets are lacking or incomplete. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14225790/s1](https://www.mdpi.com/article/10.3390/rs14225790/s1)]([https://www.mdpi.com/article/10.3390/rs14225790/s1](https://www.mdpi.com/article/10.3390/rs14225790/s1)), Figure S1: Bishkek's urban growth quantified by global datasets including the Global Human Settlement (a), World Settlement Footprint (b), and World Settlement Evolution (c); Figure S2. (a) Reference polygons used as training data for the KH-9 built-up classification. Accuracy assessment points are also shown for the KH-9 built-up classification, and the KH-9 classification masked to the built-up area classified using 2021 Sentinel-2 imagery. (b) Stable areas used to quantify DN values for masking out areas of insignificant change when investigating areas of the city that underwent redevelopment or vegetation growth. Accuracy  
Figure 13: Violin plot of built-up area proximity to active faults (Figure 1) in 1979 and 2021.  
assessment points are also shown for the DN change analysis. Accuracy assessment points for the deep learning classification applied to Sentinel-2 imagery from 2016 (c) and 2021 (d); Figure S3. Relationship between the number of building stories and building height for a sample of 300 buildings in Bishkek. Building stories were derived by counting vertical window occurrence in Google Street View (imagery from 2015). Building heights were assigned using 2013 Pleiades data (Figure 10); Figure S4. Examples of built-up area classification for the KH-9 analysis (a,b), World Settlement Footprint (c,d), and Sentinel-2 2021 (e-f). Panels in the left column (a,c,e) and right column (b,d,f) show the same area for each classification; Table S1. Optical satellite image IDs used in this study; Table S2. Comparison between field-measured building heights and those derived from the DSM-DTM difference for Pleiades and WorldView-2 data; Table S3. KH9 1979 accuracy assessment (masked to Sentinel-2 2021 built-up extent) using 500 validation points. The validation points were used to report area-adjusted classification confidence intervals and producers, users, and overall accuracy percentages; see [53,54]. Briefly, these error-adjusted metrics use the validation points to weight the classified areas accounting for omission or commission within each class. Confidence intervals are the same where the number of classes equals two, since errors are dependent. Overall accuracy is the correctly mapped proportion of the area.