## 1 Introduction  
Urban heat islands (UHIs) are becoming an increasingly significant concern for urban environments and public health. These phenomena occur when urban surfaces such as asphalt and concrete absorb and retain heat, resulting in higher temperatures compared to surrounding peri-urban areas [1]. Excessive urban heat adversely impacts various dimensions of urban systems, intensifying energy consumption and thermal stress on both human and natural environments, with a decline in air and water quality [2, 3]. As global cities continue to grow in size and population, it becomes increasingly important not to neglect the need for more accurate modeling and understanding of urban temperature dynamics and their consequences [4]. Much of the existing literature on UHIs focuses on temperatures variability--examining how temperature differences manifest seasonally or diurmally--and on assessing the public health implications associated with heat stress, heat-related illnesses, increased mortality, and elevated risks for vulnerable groups [5, 6, 7]. A further body of work explores how high temperatures affect energy demands (e.g., building cooling needs) and investigates mitigation strategies, including changes to the built environment and the integration of green spaces. Understanding these spatial patterns is essential for informed urban planning, management, and climate resilience initiatives. Effective mitigation strategies depend on methods that precisely detect the spatial distribution of UHIs and analyze the conditions that contribute to them. In the field of UHI analysis, two primary research approaches have been developed:  
* Canopy-layer UHI analysis, in which a UHI is determined by measuring air temperatures, typically about 2 m above the ground level [8];
* Surface UHI (SUHI) analysis, in which a UHI is derived from remote sensing data [9]. While the first approach requires direct measurements of the air temperature above the ground, the second approach uses remotely sensed data on the thermal emissivity of the land surface to measure land surface temperatures (LSTs). Remote sensing data and above-ground air temperature measurements are not identical but are correlated [10; 11; 12]. In the literature, the two terms UHI and SUHI have become interchangeable in describing UHI patterns. In this study, we refer to SUHI. It is important to highlight that remote sensing data offer greater spatial resolution and continuity compared to air temperature measurements. Air temperature data, typically collected by weather stations or in situ sensors, are often sparse and unevenly distributed [13], requiring interpolation techniques to estimate temperatures across broader areas. These interpolation processes, however, can introduce significant errors, especially in heterogeneous urban environments where microclimatic variations are pronounced. In contrast, satellite sensors provide consistent and high-resolution spatial coverage, capturing detailed thermal patterns across both urban and surrounding landscapes. This makes remote sensing a more reliable tool for characterizing the spatial dynamics of UHI. Furthermore, unlike satellite-derived data, which offer consistent and globally accessible measurements, air temperature data collection varies by location, depending on the availability of monitoring infrastructure or specific survey campaigns. This variability complicates the transferability of UHI detection methodologies, potentially limiting their application in comparative studies across diverse urban settlements. Ensuring methodological consistency and minimizing dependence on localized data sources are therefore critical for enhancing the robustness and scalability of UHI detection frameworks. Conventional UHI detection techniques involve a range of methods, including traditional classification methods such as threshold-based classification and spatial interpolation and advanced methods based on supervised machine learning (ML) or deep learning (DL) algorithms.