## 2 Related Work  
Traditional approaches to UHI detection present significant limitations in practical applications. Direct methods of detection, such as composite thematic classifications of LST and NDVI or threshold-based approaches, often generate substantial errors at the urban scale due to the complex and heterogeneous nature of urban environments. These methods struggle to capture the subtle variations and transitions in thermal patterns that characterize urban landscapes. More recent approaches to UHI detection employ supervised ML and DL algorithms. These techniques present significant limitations in practical application. The primary challenge lies in their dependency on extensive labeled training datasets, which require considerable time and resources to collect and validate. Recent research has begun to explore the effectiveness of clustering techniques for identifying and mapping thermal patterns and hotspots, which are essential for informed decision-making in urban environment management [15]. This progress has been facilitated by the growing availability of high-resolution urban datasets [16] and unsupervised machine learning classification techniques, as highlighted by [PERSON] et al. [17]. These unsupervised methods do not require predefined labels, making them particularly suitable in contexts where satellite data labeling is complex and costly. Overall, these developments enable a transition to quantitative \"bottom-up\" approaches [18], demonstrating significant potential for more flexible and adaptable UHI detection by identifying complex thermal structures without manual supervision. Unsupervised learning methods have been applied to a few urban studies [19], for example to identify representative building groups and predict energy use at the city scale [20], to derive a detailed morphological classification of urban forms [21], and to identify typo-morphologies and perform thermal comfort simulations with Envi-met [22]. Other studies have utilized clustering techniques to investigate the surface urban heat island (SUHI) effect at the city scale [23]. In [24], a clustering-based methodology using the principal components analysis (PCA), agglomerative hierarchical clustering (AHC), and microclimate models, is tested to map urban heat island intensity at the territory scale to support urban planning. Another study [25] proposes an unsupervised learning approach based on Gaussian mixture models (GMMs), and GIS-data are used to map urban heat island intensity across the Canton of Geneva for urban planning purposes. The authors of [26] use k-means clustering to investigate the urban heat island effect in Duran, Ecuador, and propose mitigation strategies. In [27], [PERSON] and [PERSON] introduce a functional zoning approach that utilizes weighted kernel K-means clustering to categorize urban functional zones based on thermal characteristics, thereby addressing the urban heat island phenomenon and informing urban planning and management strategies. A study by [PERSON] et al. [28] introduces a novel clustering framework, S k-means, designed for mining spatiotemporal structured climate data. This framework enhances traditional k-means clustering by addressing the complexities inherent in climate data, such as high dimensionality and temporal variations. The ISODATA clustering method is used in [[PERSON] et al., 2024] to detect urban heat islands in the city of Naples (Italy) by analyzing Landsat LST data acquired during heatwaves [29]. In Tehran, a fuzzy-based model was utilized to predict UHI locations by analyzing various contributing factors, achieving an accuracy of 78.6% in identifying high-risk areas [30]. In remote sensing, FCM is used for land use and land cover (LULC) mapping, enabling the differentiation of categories such as forests, agricultural fields, water bodies, and built-up areas [30]. It is particularly advantageous when analyzing regions with mixed pixels, where a single pixel represents multiple land cover types.