An intelligent data-processing and machine learning workflow for urban heat assessment in smart cities
2026, vol.18 , no.3, pp. 79-88
Article [2026-03-08]
Smart city management relies on practical and repeatable ways to turn spatial data into information that planners can use. Sofia, Bulgaria, was selected as a test area for the proposed satellite-based urban heat assessment. The work uses 14,400 grid observations prepared in Google Earth Engine for June 1–September 15, 2024. The dataset combines Landsat 8/9 land surface temperature, NDVI, NDBI, MODIS albedo and geographic coordinates. These variables were examined through correlation analysis and used for regression model comparison with train-test validation, five-fold cross-validation and spatial block cross-validation. Gradient Boosting achieved the best result with R² = 0.886, mean cross-validation R² = 0.890 ± 0.004 and spatial block validation R² = 0.830 ± 0.035. An additional 2023 check confirmed the same best-performing model but with lower validation scores, showing that model performance is year-dependent. The workflow supports early-stage identification of priority areas for green infrastructure.
smart cities, machine learning, remote sensing, urban heat, decision support
https://doi.org/10.59035/INRD8572
Nikolay Nikolov. An intelligent data-processing and machine learning workflow for urban heat assessment in smart cities. International Journal on Information Technologies and Security, vol.18 , no.3, 2026, pp. 79-88. https://doi.org/10.59035/INRD8572