Quantitative Assessment of Urban Sprawl Dynamics During the COVID-19 Pandemic Using AI-Supported Satellite Data
DOI:
https://doi.org/10.12974/2311-8741.2025.13.09Keywords:
Urban sprawl, Impervious surfaces, AI supported LULC, COVID-19 pandemic, Remote sensing, Spatial statisticsAbstract
This study aims to quantitatively assess the effects of socioeconomic changes experienced during the COVID-19 pandemic on urban sprawl dynamics. The research was conducted in the Döşemealtı District of Antalya Province, located in the Mediterranean Region of Türkiye, which stands out with its semi-rural urban characteristics and is part of one of the country’s most important tourism destinations. Settlement dynamics, expansion patterns of built-up areas, and their spatiotemporal changes in the study area were analyzed for the pre- and post-pandemic periods using artificial intelligence–supported land use/land cover (LULC) data. In this context, the Built-up class filtered from the ArcGIS Living Atlas LULC dataset was compared between 2017 (pre-pandemic) and 2023 (post-pandemic), and thematic maps of built-up surfaces were produced for each reference year. These maps were analyzed using geographic information system (GIS) technologies to evaluate the magnitude, spatial direction, and temporal trends of changes in impervious surfaces.The findings indicate that the spatial restructuring tendencies triggered by the pandemic reached a remarkable scale in the Döşemealtı District, with an increase in construction clusters within rural belts and a rapid conversion of vacant lands into built-up areas. Impervious surfaces, which covered 4146,94 km2 in 2017, increased to 4412,74 km2 in 2020, reached 5426,62 km2 in 2023. Accordingly, a short-term increase of 30,9% in impervious surfaces was observed, largely attributable to the pandemic period. By providing a rapid, low-cost, and objective analytical framework, this study demonstrates strong potential for application in remote sensing–based urban planning and spatial change monitoring during crisis periods. The results are expected to serve as an important data source for regional and local authorities in defining urban growth strategies, supporting sustainable planning decisions, and evaluating spatial transformations in future disaster or crisis scenarios.
References
Michalina D, Mederly P, Diefenbacher H, Held B. Sustainable urban development: A review of urban sustainability indicator frameworks. Sustainability. 2021; 13(16): 9348. https://doi.org/10.3390/su13169348
Chettry V. A critical review of urban sprawl studies. Journal of Geovisualization and Spatial Analysis. 2023; 7(2): 28. https://doi.org/10.1007/s41651-023-00158-w
Şen G. Effects of urban sprawl due to migration on spatiotemporal land use-land cover change: A case study of Bartın in Türkiye. Scientific Reports. 2025; 15(1): 815. https://doi.org/10.1038/s41598-025-85353-z
Cheng Z, Hu X. The effects of urbanization and urban sprawl on CO₂ emissions in China. Environment, Development and Sustainability. 2023; 25(2): 1792-1808. https://doi.org/10.1007/s10668-022-02123-x
Zhang Y, Wang J, Liu Y, Yue W. Quantifying multiple effects of land finance on urban sprawl: Empirical study on 284 prefectural-level cities in China. Environmental Impact Assessment Review. 2023; 101: 107156. https://doi.org/10.1016/j.eiar.2023.107156
Mun J, Lee JS, Kim S. Effects of urban sprawl on regional disparity and quality of life: A case of South Korea. Cities. 2024; 151: 105125. https://doi.org/10.1016/j.cities.2024.105125
Baqa MF, Chen F, Lu L, Qureshi S, Tariq A, Wang S, et al. Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: A case study of Karachi, Pakistan. Land. 2021; 10(7): 700. https://doi.org/10.3390/land10070700
Shao Z, Sumari NS, Portnov A, Ujoh F, Musakwa W, Mandela PJ. Urban sprawl and its impact on sustainable urban development: A combination of remote sensing and social media data. Geo-Spatial Information Science. 2021; 24(2): 241-255. https://doi.org/10.1080/10095020.2020.1787800
Petersen JK, Winkler RL, Mockrin MH. Changes to rural migration in the COVID-19 pandemic. Rural Sociology. 2024; 89(1): 130-155. https://doi.org/10.1111/ruso.12530
González-Leonardo M, Rowe F, Fresolone-Caparrós A. Rural revival? The rise in internal migration to rural areas during the COVID-19 pandemic: Who moved and where? Journal of Rural Studies. 2022; 96: 332-342. https://doi.org/10.1016/j.jrurstud.2022.11.006
Knuepling L, Sternberg R, Otto A. Rural areas as winners of COVID-19, digitalization and remote working? Empirical evidence from recent internal migration in Germany. Cambridge Journal of Regions, Economy and Society. 2025; 18(1): 227-248. https://doi.org/10.1093/cjres/rsae033
Zhang D, Yiwen Z, Fu G. Understanding counter-urbanization and re-urbanization in pandemic: Insights from people’s migration behavior in China. Habitat International. 2024; 150: 103116. https://doi.org/10.1016/j.habitatint.2024.103116
Hovardaoğlu O, Calisir-Hovardaoğlu S. Tendency of older adults to leave big cities in the COVID-19 pandemic: A qualitative regional analysis in Turkey. Regional Science Policy & Practice. 2023; 15(3): 585-606. https://doi.org/10.1111/rsp3.12561
Wang C, Hua X. Reflections on the impacts of the COVID-19 pandemic on rural livelihoods. Frontiers in Sustainable Food Systems. 2025; 9: 1655602. https://doi.org/10.3389/fsufs.2025.1655602
Chen Y. Monitoring land use/land cover change (LULCC) using remote sensing. E3S Web of Conferences. 2023; 424: 03002. https://doi.org/10.1051/e3sconf/202342403002
Babayeva NA, Amenzade RB, Rustamov RB. Space technology advances application in town planning. Journal of Environmental Science and Engineering Technology. 2014; 2(1): 9-13. https://doi.org/10.12974/2311-8741.2014.02.01.2
Kalogiannidis S, Spinthiropoulos K, Kalfas D, Chatzitheodoridis F, Tziampazi F. Integration of remote sensing and GIS for urban sprawl monitoring in European cities. European Journal of Geography. 2025; 16(2): 75-90.
Selim S, Çağlar S, Kurt A, Sönmez NK. Spatio-temporal monitoring of land use/land cover (LULC) change from classified images supported by artificial intelligence at the provincial scale. In: Current Approaches in Architecture, Planning and Design. Platanus Publishing; 2025. p. 31-47.
Wolday F, Böcker L. Exploring changes in residential preference during COVID-19: Implications to contemporary urban planning. Environment and Planning B: Urban Analytics and City Science. 2023; 50(5): 1280-1297. https://doi.org/10.1177/23998083231164398
Tesfaye W, Elias E, Warkineh B, Tekalign M, Abebe G. Modeling of land use and land cover changes using Google Earth Engine and machine learning approach: Implications for landscape management. Environmental Systems Research. 2024; 13(1): 31. https://doi.org/10.1186/s40068-024-00366-3
Hamurcu AU, Yılmaz M. Geostatistical assessment of the built environment and spatio-temporal distribution patterns of COVID-19 cases in Istanbul, Türkiye. Building and Environment. 2023; 243: 110666. https://doi.org/10.1016/j.buildenv.2023.110666
Vij A, Ardeshiri A, Crommelin L, Denham T, Washington L, Beer A. COVID-19, shifting urban growth dynamics and preferences for regional living in Australia. Urban Policy and Research. 2024; 42(1): 35-58. https://doi.org/10.1080/08111146.2024.2311667
Cuadros DF, Branscum AJ, Mukandavire Z, Miller FD, MacKinnon N. Dynamics of the COVID-19 epidemic in urban and rural areas in the United States. Annals of Epidemiology. 2021; 59: 16-20. https://doi.org/10.1016/j.annepidem.2021.04.007
Colomb C, Gallent N. Post-COVID-19 mobilities and the housing crisis in European urban and rural destinations: Policy challenges and research agenda. Planning Practice & Research. 2022; 37(5): 624-641. https://doi.org/10.1080/02697459.2022.2119512
Venter C, Mahendra A, Lionjanga N. Urban expansion and mobility on the periphery in the global South. In: Urban Form and Accessibility. Amsterdam: Elsevier; 2021. p. 243-264. https://doi.org/10.1016/B978-0-12-819822-3.00013-4
Biglieri S, De Vidovich L, Keil R. City as the core of contagion? Repositioning COVID-19 at the social and spatial periphery of urban society. Cities & Health. 2021; 5(Suppl 1): S63-S65. https://doi.org/10.1080/23748834.2020.1788320
Antipova A, Momeni E, Banai R. Urban sprawl, blight, and the COVID-19 pandemic. In: Advances in Urbanism, Smart Cities, and Sustainability. Boca Raton: CRC Press; 2022. p. 263-281. https://doi.org/10.1201/9781003126195-19
Gardner A. The shifting gravity of the urban: Cities and migration after COVID-19. In: Handbook of Research on Migration, COVID-19 and Cities. Cheltenham: Edward Elgar Publishing; 2025. p. 73-87. https://doi.org/10.4337/9781035301232.00012
Selim S, Eyileten B, Karakuş N. Investigation of green space cooling potential on land surface temperature in Antalya city of Turkey. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2023; 48: 107-114. https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-107-2023
Weather Spark. Antalya bölgesinde yıl boyu iklim ve hava durumu [Internet]. 2025 [cited 2025 Dec 15]. Available from: https: //tr.weatherspark.com/y/96456/Antalya-T%C3%BCrkiye-Ortalama-Hava-Durumu-Y%C4%B1l-Boyunca
Karra K, Kontgis C, Statman-Weil Z, Mazzariello JC, Mathis M, Brumby SP. Global land use/land cover with Sentinel-2 and deep learning. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS); 2021; IEEE. p. 4704-4707. https://doi.org/10.1109/IGARSS47720.2021.9553499
ESRI. Esri global land use/land cover (LULC) maps—Sentinel-2, deep learning-based classification [Internet]. Redlands (CA): Environmental Systems Research Institute; 2023 [cited 2025 Dec 15]. Available from: https: //livingatlas.arcgis.com/en/browse/#categories=land-use-land-cover
Narin ÖG. Comparison of ESRI Land Cover and Dynamic World: The case of Cyprus Island. Journal of Geodesy and Geoinformation. 2024; 11(1): 19-29. https://doi.org/10.9733/JGG.2024R0002.T
Li L, Yu Q, Gao L, Yu B, Lu Z. The effect of urban land-use change on runoff water quality: a case study in Hangzhou City. International Journal of Environmental Research and Public Health. 2021, 13; 18(20): 10748. https://doi.org/10.3390/ijerph182010748
TUIK, Turkish Statistical Institute (TurkStat). Building permit statistics: Number of buildings issued permits in Antalya Province and Döşemealtı District, 2017-2023 [Internet]. Ankara: TurkStat; 2025 [cited 2025 Dec 15]. Available from: https: //data.tuik.gov.tr
Mouratidis K. How COVID-19 reshaped quality of life in cities: A synthesis and implications for urban planning. Land Use Policy. 2021; 111: 105772. https://doi.org/10.1016/j.landusepol.2021.105772
Santana C, Botta F, Barbosa H, Privitera F, Menezes R, Di Clemente R. COVID-19 is linked to changes in the time-space dimension of human mobility. Nature Human Behaviour. 2023; 7(10): 1729-1739. https://doi.org/10.1038/s41562-023-01660-3
Rowe F, González-Leonardo M, Champion T. Virtual special issue: Internal migration in times of COVID-19. Population, Space and Place. 2023; 29(7): e2652. https://doi.org/10.1002/psp.2652
Jansen T, Ascani A, Faggian A, Palma A. Remote work and location preferences: A study of post-pandemic trends in Italy. Annals of Regional Science. 2024; 73(3): 897-944. https://doi.org/10.1007/s00168-024-01295-w
Prenzel P, Tomczyk S. How exogenous shocks may shape regional quality of life: A dynamic perspective on migration determinants during the COVID-19 pandemic. Review of Regional Research. 2025; 45(3): 505-537. https://doi.org/10.1007/s10037-025-00244-0
Giles-Corti B, Foster S, Lynch B, Lowe M. What are the lessons from COVID-19 for creating healthy, sustainable, resilient future cities? NPJ Urban Sustainability. 2023; 3(1): 29. https://doi.org/10.1038/s42949-023-00107-y
Amirzadeh M, Sobhaninia S, Buckman ST, Sharifi A. Towards building resilient cities to pandemics: A review of COVID-19 literature. Sustainable Cities and Society. 2023; 89: 104326. https://doi.org/10.1016/j.scs.2022.104326
Johnson KK, Parton L, Nolte C, Williamson M, Nogeire-McRae T, Paudel J, Brandt J. Moving to the country: Understanding the effects of COVID-19 on property values and farmland development risk. Journal of Housing Economics. 2023; 62: 101955. https://doi.org/10.1016/j.jhe.2023.101955
Wesz JGB, Miron LIG, Delsante I, Tzortzopoulos P. Urban quality of life: A systematic literature review. Urban Science. 2023; 7(2): 56. https://doi.org/10.3390/urbansci7020056
Rezvani S, Falcão MJ, Komljenovic D, de Almeida NM. A systematic literature review on urban resilience enabled with asset and disaster risk management approaches and GIS-based decision support tools. Applied Sciences. 2023; 13(4): 2223. https://doi.org/10.3390/app13042223
Zarro C, Cerra D, Auer S, Ullo SL, Reinartz P. Urban sprawl and COVID-19 impact analysis by integrating deep learning with Google Earth Engine. Remote Sensing. 2022; 14(9): 2038. https://doi.org/10.3390/rs14092038
Irfan A, Li Y, Xinhua E, Sun G. Land Use and Land Cover classification with deep learning-based fusion of SAR and optical data. Remote Sensing. 2025; 17(7): 1298. https://doi.org/10.3390/rs17071298
Lesiv M, Fritz S, Dürauer M, Georgieva I, Buchhorn M, Bertels L, Tsendbazar N, Van De Kerchove R, Zanaga D, Schepaschenko D, See L. A global reference data set for land cover mapping at 10 m resolution. Earth System Science Data Discussions. 2025; 21: 1-9. https://doi.org/10.3390/rs17071298
Jocea AF, Porumb L, Necula L, Raducanu D. Sentinel-2 Land Cover Classification: State-of-the-Art Methods and the Reality of Operational Deployment—A Systematic Review. Sustainability. 2025; 15; 17(22). https://doi.org/10.3390/su172210324
Vali A, Comai S, Matteucci M. Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sensing. 2020; 3; 12(15): 2495. https://doi.org/10.3390/rs12152495
