## 3 Results and Discussion
This contribution is relevant, deepening and complementing the findings obtained in [11], where certain reflectance bands were identified based on the spectral signature of asbestos, which may be relevant in differentiation from various materials. Similarly, the results in this article are a significant contribution to what was presented in [42], where the aim is to differentiate different types of asbestos roofs using principal component analysis techniques, along with the comparison of reflectance curves using local peaks and maximum differences obtained in these curves. The present study delves into an innovative approach to identifying the condition of asbestos-cement roofs in urban environments. As the authors explore the intricacies of hazardous material management in construction and demolition, the current research emphasizes the pivotal role played by machine learning. It is crucial to acknowledge the historical emphasis on evaluating specific methods for asbestos-cement roof detection in the scientific literature. Table 5 presents the main results of a review conducted on Science Direct, Springer Link, Scopus, MDPI, and other web sources such as Google Scholar and ResearchGate, encompassing almost two decades of studies in remote sensing applied to identifying AC roofs. Conventional methods like SAM were the most common, albeit time-consuming, computationally expensive, and incurring significant software costs. Conversely, the use of machine learning appears to partially address these issues, offering more streamlined solutions as the quality, resolution, and weight of available images increase. Table 5 further highlights the scarcity of studies in the literature utilizing hyperspectral images with acceptable resolution in the SWIR range. Additionally, it is uncommon to encounter studies that compare more than two or three machine learning methods. In contrast, the present study conducts a comparative analysis of five widely recognized methods, frequently applied in the literature and across various scientific disciplines. Previous works, such as those by [PERSON] et al. (2015) [21] and [PERSON] et al. (2007) [24], made valuable efforts to identify the condition of these roofs through various types of images. In particular, [PERSON] et al. (2015) [21] demonstrated the potential of using hyperspectral remote sensing data for AC roof identification. A supervised classification based on images using the Spectral Angle Mapper (SAM) algorithm was employed. The identification of AC roofs achieved a user accuracy of 86% and a producer accuracy of 89%. An innovative index designed to distinguish between roofs with different levels of deterioration was introduced, based on the level of chlorophyll present on the roof, proposing prioritized removal interventions. According to this Index of Surface Deterioration (ISD), it was determined that 40% of asbestos roofs presented a high removal priority. On the other hand, [PERSON] et al. (2007) [24] highlight the effectiveness of remote sensing in identifying sheets of AC roofs. Specifically, the feasibility of determining the deterioration status of asbestos roofs based on the amount of exposed mineral fibers was evaluated. Multi- and hyperspectral data in the SWIR and/or thermal infrared (TIR) bands were used for this purpose. The significance of these findings lies in the confirmation, supported by the medical literature, that the dispersion of asbestos fibers in the air can be a pathogenic agent associated with lung cancer and other asbestos-related diseases [43]. A sensitivity analysis, focused on the band depth of absorption features related to asbestos fiber variations on the surface, revealed a significant linear regression (with an R2 greater than 0.90) at 2.32 \(\upmu\)m and 9.44 \(\upmu\)m. Additionally, the spectral characteristics of the samples were used to simulate the most appropriate instrumental conditions for detecting asbestos absorption features in both SWIR and TIR, according to the detection limit analysis.