Applying Very High Resolution Satellite Imagery in Nowcasting
Abstract

This paper explores the application of very high-resolution (VHR) satellite imagery for economic nowcasting. VHR optical satellite imagery is globally available in near real-time, offering a valuable alternative data source for regions with limited conventional data, especially those affected by crises. By analyzing changes in vehicle numbers, my findings accurately reflect the shifts in Kyiv’s economic activity immediately following the full-scale russian invasion. This observed change was primarily by population displacement.

Publication History
Avaliable online 26 November 2025
19
views
Full Text
Citation
Cite as: Lennart, J. (2025). Applying Very High Resolution Satellite Imagery in Nowcasting. Visnyk of the National Bank of Ukraine (Occasional Papers, 2025/03). https://doi.org/10.26531/vnbu2025.op03
Citation Format

Metrics
References

Abrahams, A., Oram, C., Lozano-Gracia, N. (2018). Deblurring DMSP nighttime lights: A new method using Gaussian filters and frequencies of illumination. Remote Sensing of Environment, 210, 242–258. https://doi.org/10.1016/j.rse.2018.03.018

Altman, H. (2023). Ukraine bought ‘All The Gas Trucks Available In Europe’ to keep fueled (updated). The War Zone, 15 February. https://www.twz.com/ukraine-bought-all-the-gas-trucks-available-in-europe-to-keep-fueled

Armstrong, J. S. (2001). Combining forecasts. In: Armstrong, J.S. (eds) Principles of Forecasting. International Series in Operations Research & Management Science, 30, , pp. 417–439. Springer, Boston, MA. https://doi.org/10.1007/978-0-306-47630-3_19

Arshad, S., Beyer, R. C. M. (2023). Tracking economic fluctuations with electricity consumption in Bangladesh. Energy Economics, 123, 106740. https://doi.org/10.1016/j.eneco.2023.106740

Askitas, N., Zimmermann, K. F. (2013). Nowcasting business cycles using toll data. Journal of Forecasting, 32(4), 299–306. https://doi.org/10.1002/for.1262

Bachmann, R., Baqaee, D., Bayer, C. et al. (2022). How it can be done. ECONtribute Policy Brief Series, 034. https://www.econtribute.de/RePEc/ajk/ajkpbs/ECONtribute_PB_034_2022_EN.pdf

Bec, F., Mogliani, M. (2015). Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information? International Journal of Forecasting, 31(4), 1021–1042. https://doi.org/10.1016/j.ijforecast.2014.11.006

Beck, G. W., Carstensen, K., Menz, J.-O., Schnorrenberger, R., Wieland, E. (2023). Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany. Deutsche Bundesbank Discussion Paper, 34. https://www.econstor.eu/bitstream/10419/282982/1/1877230987.pdf

Beyzatlar, M. A., Karacal, M., Yetkiner, H. (2014). Granger-causality between transportation and GDP: A panel data approach. Transportation Research Part A: Policy and Practice, 63, 43–55. http://doi.org/10.1016/j.tra.2014.03.001

Bickenbach, F., Bode, E., Nunnenkamp, P., Söder, M. (2016). Night lights and regional GDP. Review of World Economics, 152, 425–447. https://doi.org/10.1007/s10290-016-0246-0

Bok, B., Caratelli, D., Giannone, D., Sbordone, A., Tambalotti, A. (2018). Macroeconomic nowcasting and forecasting with Big Data. Annual Review of Economics, 10. https://doi.org/10.1146/annurev-economics-080217-053214

Borko, T., Fradynskyi, O., Oneshko, S., Zalievska-Shyshak, A., Krasota, O. (2022). Prospects for restoring the economic potential of Ukraine in the post-war period. Economic Affairs, 67(04s), 815–823. https://doi.org/10.46852/0424-2513.4s.2022.15

Brakman, S., Garretsen, H., Schramm, M. (2004). The strategic bombing of German cities during World War II and its impact on city growth. Journal of Economic Geography, 4(2), 201–218. https://doi.org/10.1093/jeg/4.2.201

Brauer, J. (2017). “Of the Expence of Defence”: What has changed since Adam Smith? Peace Economics, Peace Science, and Public Policy, 23(2), 20170012. https://doi.org/10.1515/peps-2017-0012

Burke, M., Driscoll, A., Lobell, D. B., Ermon, S. (2021). Using satellite imagery to understand and promote sustainable development. Science, 371, 6535. https://doi.org/10.1126/science.abe8628

Cascaldi-Garcia, D., Luciani, M., Modugno, M. (2023). Lessons from nowcasting GDP across the world. International Finance Discussion Papers, 1385. https://www.federalreserve.gov/econres/ifdp/files/ifdp1385.pdf

Chen, X., Nordhaus, W. (2019). VIIRS nighttime lights in the estimation of cross-sectional and time-series GDP. Remote Sensing, 11(9), 1057. https://doi.org/10.3390/rs11091057

Chetty, R., Friedman, J. N., Stepner, M., Opportunity Insights Team (2023). The economic impacts of COVID-19: Evidence from a new public database built using private sector data. The Quarterly Journal of Economics, 139(2), 829–889. https://doi.org/10.1093/qje/qjad048

Cimadomo, J., Giannone, D., Lenza, M., Monti, F., Sokol, A. (2022). Nowcasting with large Bayesian vector autoregressions. Journal of Econometrics, 231(2), 500–519. https://doi.org/10.1016/j.jeconom.2021.04.012

Constantinescu, M., Kappner, K., Szumilo, N. (2024). The Warcast Index: Estimating Economic Activity without Official Data during the Ukraine War in 2022. NBU Working Papers, 3/2024. Kyiv: National Bank of Ukraine. https://bank.gov.ua/admin_uploads/article/WP_2024-03_Constantinescu.pdf

Digital Globe (2016). Accuracy of WorldView Products.

Diwan, T., Anirudh, G., Tembhurne, J. V. (2023). Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Toolsand Applications, 82(6), 9243–9275. https://doi.org/10.1007/s11042-022-13644-y

Do, L. P. C., Lin, K.-H., Molnár, P. (2016). Electricity consumption modelling: A case of Germany. Economic Modelling, 55, 92–101. https://doi.org/10.1016/j.econmod.2016.02.010

Donaldson, D., Storeygard, A. (2016). The view from above: Applications of satellite data in economics. Journal of Economic Perspectives, 30(4), 171–198. https://doi.org/10.1257/jep.30.4.171

Engstrom, R., Hersh, J., Newhouse, D. (2022). Poverty from space: Using high resolution satellite imagery for estimating economic well-being. The World Bank Economic Review, 36(2), 382–412. https://doi.org/10.1093/wber/lhab015

Eun, J., Skakun, S. (2022). Characterizing land use with night-time imagery: The war in Eastern Ukraine (2012–2016). Environmental Research Letters, 17(9), 095006. https://doi.org/10.1088/1748-9326/ac8b23

European Commission (2023). Ukraine 2023 Report: [Commision Stafff Working Document]. https://enlargement.ec.europa.eu/ukraine-report-2023_en

Fezzi, C., Fanghella, V. (2021). Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe. European Economic Review, 139, 103907. https://doi.org/10.1016/j.euroecorev.2021.103907

Gaudry, M., Fridstrøm, L. (2023). Road traffic intensity of GDP and the explanation of national peaks of yearly road fatalities and of their clustering in 1970−1974. Emergency Management ]Science and Technology, 3, 6. https://doi.org/10.48130/EMST-2023-0006

Ghosh, T., Powell, R. L., Elvidge, C. D., Baugh, K. E., Sutton, P. C., Anderson, S. (2010). Shedding light on the global distribution of economic activity. The Open Geography Journal, 3, 147–160. http://doi.org/10.2174/1874923201003010147

Giannone, D., Reichlin, L., Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665–676. https://doi.org/10.1016/j.jmoneco.2008.05.010

Gibson, J., Olivia, S., Boe-Gibson, G., Li, C. (2021). Which night lights data should we use in economics, and where? Journal of Development Economics, 149, 102602. https://doi.org/10.1016/j.jdeveco.2020.102602

Goldblatt, R., Heilmann, K., Vaizman,Y Y. (2020). Can medium-resolution satellite imagery measure economic activity at small geographies? Evidence from Landsat in Vietnam. The World Bank Economic Review, 34(3), 635–653. https://doi.org/10.1093/wber/lhz001

Gorodnichenko, Y., Korhonen, I., Ribakova, E. (2024). The Russian economy on a war footing: A new reality financed by commodity exports. CEPR Policy Insight, 1. https://cepr.org/voxeu/columns/russian-economy-war-footing-new-reality-financed-commodity-exports

Henderson, V., Storeygard, A., Weil, D. N. (2011). A bright idea for measuring economic growth”. The American Economic Review, 101(3), 194–199. https://doi.org/10.1257/aer.101.3.194

Hössinger, R., Link, C., Sonntag, A., Stark, J. (2017). Estimating the price elasticity of fuel demand with stated preferences derived from a situational approach. Transportation Research Part A: Policy and Practice, 103, 154–171. https://doi.org/10.1016/j.tra.2017.06.001

Hrabynskyi, I., Prykhodko, I., Halanets, V., Prokopyshyn-Rashkevych, L., Adamovsky, A., Zhygalo, I. (2022). The impact of the Russian-Ukrainian war on the development of the primary residential real estate market in Ukraine: Results of a cluster analysis. Economic Affairs, 67(4s), 837–849. https://doi.org/10.46852/0424-2513.4s.2022.17

Iacono, M., Levinson, D. (2016). Mutual causality in road network growth and economic development. Transport Policy, 45, 209–217. https://doi.org/10.1016/j.tranpol.2015.06.005

Jain, M. (2020). The benefits and pitfalls of using satellite data for causal inference. Review of Environmental Economics and Policy, 14(1), 157–169. https://doi.org/10.1093/reep/rez023

Jiang, W., He, G., Long, T., Liu, H. (2017). Ongoing conflict makes Yemen dark: From the perspective of nighttime light. Remote Sensing, 9(8), 798. https://doi.org/10.3390/rs9080798

Kimbrough, E. O., Laughren, K., Sheremeta, R. (2020). War and conflict in economics: Theories, applications, and recent trends. Journal of Economic Behavior & Organization, 178, 998–1013. https://doi.org/10.1016/j.jebo.2017.07.026

KSE (2024). Report on Damages to Infrastructure from the Destruction Caused by Russia’s Military Aggression Against Ukraine as of January 2024. Last accessed: 2024-09-07. https://kse.ua/wp-content/uploads/2024/05/Eng_01.01.24_Damages_Report.pdf

Kuznets, S. (1934). National income, 1929-1932. National Bureau of Economic Research. http://www.nber.org/books/kuzn34-1

Landefeld, J. S., Seskin, E. P., Fraumeni, B. M. (2008). Taking the pulse of the economy: Measuring GDP”. Journal of Economic Perspectives, 22(2), 193–216. https://doi.org10.1257/jep.22.2.193

Lehmann, R., Möhrle, S. (2024). Forecasting regional industrial production with novel high-frequency electricity consumption data. Journal of Forecasting, 43(6), 1918–1935. https://doi.org/10.1002/for.3116

Lehnert, P., Niederberger, M., Backes-Gellner, U., Bettinger, E. (2023). Proxying economic activity with daytime satellite imagery: Filling data gaps across time and space. PNAS Nexus, 2(4), pgad099. https://doi.org/10.1093/pnasnexus/pgad099

Li, B., Gao, S., Liang, Y., Kang, Y., Prestby, T., Gao, Y., Xiao, R. (2020). Estimation of regional economic development indicator from transportation network analytics. Scientific Reports, 10(1), 2647. https://doi.org/10.1038/s41598-020-59505-2

Li, X., Li, D. (2014). Can night-time light images play a role in evaluating the Syrian Crisis? International Journal of Remote Sensing, 35(18), 6648–6661. https://doi.org/10.1080/01431161.2014.971469

Liadze, I., Macchiarelli, C., Mortimer-Lee, P., Sanchez Juanino, P. (2023). Economic costs of the Russia-Ukraine war. The World Economy, 46(4), 874–886. https://doi.org/10.1111/twec.13336

Liu, K., Mattyus, G. (2015). Fast multiclass vehicle detection on aerial images. IEEE Geoscience and Remote Sensing Letters, 12(9), 1938-1942. https://doi.org/10.1109/lgrs.2015.2439517

Liu, Z., Yin, H., Wu, X., Wu, Z., Mi, Y., Wang, S. (2021). From shadow generation to shadow removal. arXiv:2103.12997v1. https://doi.org/10.48550/arXiv.2103.12997

Manakos, I. (2003). Information sableitung aus ”Off Nadir” Reflexionsaufnahmen zur Entscheidungsunterstützung in ”Precision Agriculture”. PhD thesis. Technische Universität München.

Martínez, L. R. (2022). How much should we trust the dictator’s GDP growth estimates? The Journal of Political Economy, 132(10), 2731–2769. https://doi.org/10.1086/720458

Matsumura, K., Oh, Y., Sugo, T., Takahashi, K. (2024). Nowcasting economic activity with mobility data. Journal of the Japanese and International Economies, 73, 101327. https://doi.org/10.1016/j.jjie.2024.101327

Michalopoulos, S., Papaioannou, E. (2014). National institutions and subnational development in Africa. The Quarterly Journal of Economics, 129(1), 151–213. https://doi.org/10.1093/qje/qjt029

Mundhenk, T. N., Konjevod, G., Sakla, W. A., Boakye, K. (2016). A large contextual dataset for classification, detection and counting of cars with deep learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision – ECCV 2016. Lecture Notes in Computer Science, 9907. Springer. https://doi.org/10.1007/978-3-319-46487-9_48

Mykhailyshyna, D. (2023). A survey of Ukrainian refugees. German Economic Team Newsletter, 173. https://www.german-economic-team.com/en/newsletter/a-survey-of-ukrainian-refugees/

Mykhnenko, V., Delahaye, E., Mehdi, N. (2022). Understanding forced internal displacement in Ukraine: insights and lessons for today’s crises. Oxford Review of Economic Policy 38(3), pp. 699–716. https://doi.org/10.1093/oxrep/grac020

Nordhaus, W. (2002). The economic consequences of a war in Iraq. NBER Working Paper, w9361. https://doi.org/10.3386/w9361

Nordhaus, W., Chen, X. (2015). A sharper image? Estimates of the precision of nighttime lights as a proxy for economic statistics. Journal of Economic Geography, 15(1), 217–246. https://doi.org/10.1093/jeg/lbu010

OECD (2022). Anti-Corruption Reforms in Ukraine: Pilot 5th Round of Monitoring Under the OECD Istanbul Anti-Corruption Action Plan.

Ostapenko, N., Williams, C. C. (2016). Determinants of entrepreneurs’ views on the acceptability of tax evasion and the informal economy in Slovakia and Ukraine: An institutional asymmetry approach. International Journal of Entrepreneurship and Small Business, 28(2/3), 275–289. https://doi.org/10.1504/IJESB.2016.076639

Pappalardo, L., Vanhoof, M., Gabrielli, L., Smoreda, Z., Pedreschi, D., Giannotti, F. (2016). An analytical framework to nowcast well-being using mobile phone data. International Journal of Data Science and Analytics, 2, 75–92. https://doi.org/10.1007/s41060-016-0013-2

Parady, G., Suzuki, K., Oyama, Y., Chikaraishi, M. (2023). Activity detection with Google Maps Location History data: Factors affecting joint activity detection probability and its potential application on real social networks. Travel Behaviour and Society, 30, 344–357. https://doi.org/10.1016/j.tbs.2022.10.010

Pethick-Lawrence, F. (1915). War economics. The Economic Journal, 25(100), 512–520. https://doi.org/10.2307/2221590

Phan, D. H. (2023). Lights and GDP relationship: What does the computer tell us? Empirical Economics, 65, 1215–1252. https://doi.org/10.1007/s00181-023-02377-y

Pigou, A. C. (1940). War finance and inflation. The Economic Journal, 50(200), 461–468. https://doi.org/10.2307/2226205

Proietti, T., Giovannelli, A., Ricchi, O., Citton, A., Tegami, C., Tinti, C. (2021). Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach. International Journal of Forecasting, 37(4), 1376–1398. https://doi.org/10.1016/j.ijforecast.2021.04.003

Proietti, T., Pedregal, D. J. (2023). Seasonality in high frequency time series. Econometrics and Statistics, 27, 62–82. https://doi.org/10.1016/j.ecosta.2022.02.001

Pulina, T., Joukl, M., But, T. (2023). The influence of the labor potential of the Ukrainian population’s migration to the EU countries during the war. Academy Review, 58(1), 220– 230. https://doi.org/10.32342/2074-5354-2023-1-58-16

Qadir, J., Ali, A., ur Rasool, R., Zwitter, A., Sathiaseelan, A., Crowcroft, J. (2016). Crisis analytics: Big data-driven crisis response. Journal of International Humanitarian Action, 1, 12. https://doi.org/10.1186/s41018-016-0013-9

Razakarivony, S., Jurie, F. (2016). Vehicle detection in aerial imagery: A small target detection benchmark. Journal of Visual Communication and Image Representation, 34, 187–203. https://doi.org/10.1016/j.jvcir.2015.11.002

Román, M. O., Wang, Z., Sun, Q. … et al. (2018). NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment, 210, 113–143. https://doi.org/10.1016/j.rse.2018.03.017

Sarmadi, H., Wahab, I., Hall, O., Rögnvaldsson, T., Ohlsson, M. (2024). Human bias and CNNs’ superior insights in satellite based poverty mapping. Scientific Reports, 14(1), 22878. https://doi.org/10.1038/s41598-024-74150-9

Schippers, V., Botzen, W. (2023). Uncovering the veil of night light changes in times of catastrophe. Natural Hazards and Earth System Sciences, 23(1), 179–204. https://doi.org/10.5194/nhess-23-179-2023

Schmidt, F. (2012). Data Set for Tracking Vehicles in Aerial Image Sequences. KIT. https://www.ipf.kit.edu/downloads_data_set_AIS_vehicle_tracking.php

Schumann, A. (2014). Persistence of population shocks: Evidence from the occupation of West Germany after World War ll. American Economic Journal: Applied Economics, 6(3), 189–205. https://doi.org/10.1257/app.6.3.189

Singhal, S. (2019). Early life shocks and mental health: The longterm effect of war in Vietnam. Journal of Development Economics, 141, 102244. https://doi.org/10.1016/j.jdeveco.2018.06.002

Solanko, L. (2024). Where do Russia’s mobilized soldiers come from? Evidence from bank deposits. BOFIT Policy Brief, 1/2024. Bank of Finland. https://hdl.handle.net/10419/283620

Stokes, E. C., Román, M. O. (2022). Tracking COVID-19 urban activity changes in the Middle East from nighttime lights. Scientific Reports, 12(1), 8096. https://doi.org/10.1038/s41598-022-12211-7

Stundziene, A., Pilinkiene, V., Bruneckiene, J., Grybauskas, A., Lukauskas, M. (2023). Nowcasting economic Activity using electricity market data: The case of Lithuania. Economies, 11(5), 134. https://doi.org/10.3390/economies11050134

Stuparu, D.-G., Ciobanu, R.-I., Dobre, C. (2020). Vehicle detection in overhead satellite images using a one-stage object detection model. Sensors, 20(22), 6485. https://doi.org/10.3390/s20226485

Sutton, P. C. (2003). A scale-adjusted measure of “Urban sprawl” using nighttime satellite imagery. Remote Sensing of Environment, 86(3), 353–369. https://doi.org/10.1016/S0034-4257(03)00078-6

Testa, P. A. (2021). “The economic legacy of expulsion: Lessons from post-war Czechoslovakia. The Economic Journal, 131(637), 2233–2271. https://doi.org/10.1093/ej/ueaa132

The Economist, Solstad, S. (2023). The Economist War-Fire Model [First published in the article "A hail of destruction", The Economist, February 25th issue, 2023]. https://github.com/TheEconomist/the-economist-war-fire-model

Trebesch, C., Antezza, A., Bushnel, K. et al. (2024). The Ukraine Support Tracker: Which countries help Ukraine and how? Kiel Working Paper, 2218. https://www.kielinstitut.de/fileadmin/Dateiverwaltung/IfW-Publications/fis-import/3ce24028-f1f9-4a1c-b456-2ef810d22188-KWP_2218_Trebesch_et_al_Ukraine_Support_Tracker.pdf

Wang, S., Li, X. (2024). Use of nighttime light images in evaluating refugee settlement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 14122–14132.

Weber, M. (1922). Wirtschaft Und Gesellschaft. Tübingen: Mohr.

Węcel, K., Stróżyna, M., Szmydt, M., Abramowicz, W. (2024). The impact of crises on maritime traffic: A case study of the COVID-19 pandemic and the war in Ukraine. Networks and Spatial Economics, 24(1), 199–230. https://doi.org/10.1007/s11067-023-09612-0

Williams, C. C., Schneider, F. (2016). Measuring the Global Shadow Economy: The Prevalence of Informal Work and Labour. Edward Elgar Publishing. https://doi.org/10.4337/9781784717995

Wüpper, D., Oluoch, W. A., Hadi, H. (2024). Satellite data in agricultural and environmental economics: Theory and practice. Agricultural Economic, 56(3), 493–511. https://doi.org/10.1111/agec.70006

Xia, G.-S., Bai, X., Ding, J. et al. (2018). DOTA: A large-scale dataset for object detection in aerial images. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 3974-3983. https://doi.org/10.1109/CVPR.2018.00418

Zheng, S., Kahn, M. E. (2013). China’s bullet trains facilitate market integration and mitigate the cost of megacity growth. Proceedings of the National Academy of Sciences, 110(14), E1248–E1253. https://doi.org/10.1073/pnas.1209247110

Rights and Permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.