Evaluating urban fabric transformations using GeoAI
DOI:
https://doi.org/10.6093/1970-9870/11344Keywords:
GeoAI, Remote Sensing, Urban Fabric TransformationsAbstract
Urban growth has reshaped land use patterns globally, demanding robust and scalable methodologies to monitor its long-term dynamics. This study proposes a GeoAI-based framework that integrates Random Forest (RF) classification with spatial indicators to analyze urban fabric transformations in Ravenna, northern Italy, from 2000 to 2024. Using Landsat 5 and Landsat 9 multispectral imagery processed in the Google Earth Engine (GEE) cloud computing platform, six Land Use and Land Cover (LULC) classes were mapped with high accuracy. The RF classifier achieved an overall accuracy of 86.2% in 2024, confirming its suitability for complex urban environments. The classified maps were imported into a GIS environment to extract built-up surfaces and compute spatial indicators, including Urban Density (UD), Urban Dispersion Index (UDI), Annual Growth Rate (AGR), and Urban Expansion Index (UEI). Results reveal a moderate densification in Ravenna’s urban core alongside an increase in dispersed residential nuclei, confirming a dual trend of consolidation and sprawl. The indicator values align with northern Italian urbanization trends reported in the literature. This approach demonstrates how combining supervised classification with spatial metrics can provide deeper insights into urban growth, supporting more informed planning and policy-making. The framework is scalable, reproducible, and adaptable to different urban contexts.
Downloads
References
Basheer, S., Wang, X., Farooque, A. A., Nawaz, R. A., Liu, K., Adekanmbi, T. & Liu, S. (2022). Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sensing, 14 (19), 4978. https://doi.org/10.3390/rs14194978
Benhammou, Y., Alcaraz-Segura, D., Guirado, E., Khaldi, R., Achchab, B., Herrera, F. & Tabik, S. (2022). Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning. Scientific Data, 9 (1), 681. https://doi.org/10.1038/s41597-022-01775-8
Chaturvedi, V. & de Vries, W. T. (2021). Machine learning algorithms for urban land use planning: A review. Urban Science, 5 (3), 68. https://doi.org/10.3390/urbansci5030068
Fistola, R. & La Rocca, R. A. (2024). From smart city to artificial intelligence city. Envisaging the future of urban planning. TeMA - Journal of Land Use, Mobility and Environment, 17 (3), 413-424. http://dx.doi.org/10.6093/1970-9870/11081
Francini, M., Salvo, C. & Vitale, A. (2023). Combining deep learning and multi-source GIS methods to analyze urban and greening changes. Sensors, 23 (8), 3805. https://doi.org/10.3390/s23083805
Gaglione, F. (2023). Urban planning and GeoAI in smart city policies. TeMA - Journal of Land Use, Mobility and Environment, 16 (3), 631-637. http://dx.doi.org/10.6092/1970-9870/10317
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
Khachoo, Y. H., Cutugno, M., Robustelli, U. & Pugliano, G. (2024). Impact of Land Use and Land Cover (LULC) Changes on Carbon Stocks and Economic Implications in Calabria Using Google Earth Engine (GEE). Sensors, 24 (17), 5836. https://doi.org/10.3390/s24175836
Krivoguz, D., Chernyi, S. G., Zinchenko, E., Silkin, A. & Zinchenko, A. (2023). Using Landsat-5 for accurate historical LULC classification: A comparison of machine learning models. Data, 8 (9), 138. https://doi.org/10.3390/data8090138
Le, T. D. H., Pham, L. H., Dinh, Q. T., Hang, N. T. T. & Tran, T. A. T. (2022). Rapid method for yearly LULC classification using random forest and incorporating time-series NDVI and topography: A case study of Thanh Hoa province, Vietnam. Geocarto International, 37 (27), 17200-17215. https://doi.org/10.1080/10106049.2022.2123959
Mazzeo, G. & Russo, L. (2016). Aspects of Land Take in the Metropolitan Area of Naples. TeMA - Journal of Land Use, Mobility and Environment, 9 (1), 89-107. https://doi.org/10.6092/1970-9870/3727
Partheepan, K., Musthafa, M. M. & Bhavan, T. (2023). Remote sensing investigation of spatiotemporal land-use changes. Tema. Journal of Land Use, Mobility and Environment, 16 (2), 383-402. http://dx.doi.org/10.6093/1970-9870/9908
Puttinaovarat, S., Khaimook, K. & Horkaew, P. (2023). Land use and land cover classification from satellite images based on ensemble machine learning and crowdsourcing data verification. International Journal of Cartography, 1-21. https://doi.org/10.1080/23729333.2023.2166252
QGIS Development Team (2025). QGIS Geographic Information System (version 3.36) [Computer software]. Open Source Geospatial Foundation Project. https://qgis.org/en/site/ (accessed 12 December 2025)
Romano, B., Zullo, F., Fiorini, L., Marucci, A. & Ciabò, S. (2017a). Land transformation of Italy due to half a century of urbanization. Land Use Policy, 67, 387-400. http://dx.doi.org/10.1016/j.landusepol.2017.06.006
Romano, B., Zullo, F., Fiorini, L., Ciabò, S. & Marucci, A. (2017b). Sprinkling: An approach to describe urbanization dynamics in Italy. Sustainability, 9 (1), 97. https://doi.org/10.3390/su9010097
Salvo, C. & Vitale, A. (2024a). A web-based decision support system for sustainable urban planning and management. TERRITORIO, 106 (4), 165-176. https://doi.org/10.3280/TR2023-106019
Salvo, C. & Vitale, A. (2024b). Spatiotemporal dynamics of urban growth and greening goals towards sustainable development. In International Conference on Innovation in Urban and Regional Planning (pp. 183-195). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-54096-7_17
Samardžić-Petrović, M., Kovačević, M., Bajat, B. & Dragićević, S. (2017). Machine learning techniques for modelling short term land-use change. ISPRS International Journal of Geo-Information, 6 (12), 387. https://doi.org/10.3390/ijgi6120387
Song, R., Feng, Y., Xing, C., Mu, Z. & Wang, X. (2022). Hyperspectral image change detection based on active convolutional neural network and spatial-spectral affinity graph learning. Applied Soft Computing, 125, 109130. https://doi.org/10.1016/j.asoc.2022.109130
Velastegui-Montoya, A., Montalván-Burbano, N., Carrión-Mero, P., Rivera-Torres, H., Sadeck, L. & Adami, M. (2023). Google Earth Engine: A global analysis and future trends. Remote Sensing, 15 (14), 3675. https://doi.org/10.3390/rs15143675
Vitale, A. (2025). A GeoAI-based approach for long-term monitoring of urban fabric transformations. Acta IMEKO, 14 (2), 1-9. International Measurement Confederation (IMEKO). https://doi.org/10.21014/actaimeko.v14i2.2117
Vitale, A. & Lamonaca, F. (2025a). Advancing built-up area monitoring through multi-temporal satellite data fusion and machine learning-based geospatial analysis. Remote Sensing, 17 (11), 1830. https://doi.org/10.3390/rs17111830
Vitale, A. & Lamonaca, F. (2025b). Enhancing GeoAI land cover classification via hyperparameter tuning and cross-validation: A case study in Ravenna, Italy. Measurement, 257 Part A, 118662. https://doi.org/10.1016/j.measurement.2024.118662
Vitale, A., Salvo, C. & Lamonaca, F. (2024). A novel geospatial methodology for measuring and mapping spatiotemporal built-up dynamics based on Google Earth Engine and unsupervised K-means clustering of multispectral satellite imagery. In 2024 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv), 57-62. IEEE. https://doi.org/10.1109/metrolivenv60384.2024.10615674
Vitale, A. & Salvo, C. (2024). Monitoring and forecasting land cover dynamics using remote sensing and geospatial technology. In Italian Conference on Geomatics and Geospatial Technologies, 126-140. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-59925-5_10
Wang, J., Bretz, M., Dewan, M. A. A. & Delavar, M. A. (2022). Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges, and prospects. Science of The Total Environment, 822, 153559. https://doi.org/10.1016/j.scitotenv.2022.153559
Zafar, Z., Zubair, M., Zha, Y., Fahd, S. & Nadeem, A. A. (2024). Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data. The Egyptian Journal of Remote Sensing and Space Sciences, 27 (2), 216-226. https://doi.org/10.1016/j.ejrs.2024.03.003
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Alessandro Vitale
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
