## 2 Materials and Methods  
For the development of the present research, an adaptation to 4 phases of the methodology for data science projects known as CRISP-DM was carried out. Thus, the following methodological phases were defined: data and business understanding, data preparation, modeling and evaluation, and deployment Figure 1). Field-selected roofs displaying asbestos presence through polarized light microscopy (PLM) according to the EPA 600/R-04/004 International Standard, using a Leica DM750P microscope (Leica Microsystems Inc. Deerfield, IL 60015 United States), underwent on-site assessments to determine their deterioration status, distinguishing between high intervention priority (HIP) and low intervention priority (LIP). These priority levels indicate the urgency for intervention in asbestos-cement roofs, whether through radical measures  
Figure 1: Scheme of the methodology phases considered. like AC removal or through mitigation methods such as enclosure or encapsulation [27; 28; 29]. The conventional method employed in the field for distinguishing between HIP and LIP involves visual qualitative inspection [30; 31]. Parameters considered to assess HIP and LIP align with methodologies found in the existing literature [32; 33], with general, local, or absent presence of black, moss, vegetation cape, cracks, fissures, or holes, poor state of the edges, and surface release of fibers by hand. Consequently, widespread surface presence of one or more deterioration parameters categorizes a roof as HIP due to the heightened likelihood of releasing asbestos fibers into the environment [24; 34]. Conversely, AC roofs exhibiting localized or absent deterioration are classified as LIP. For a detailed discussion on distinguishing between HIP and LIP, please refer to the author's previous work [35]. Therefore, in the model and data comprehension phase, data corresponding to 72 asbestos-cement tiles (48 with HIP and 24 with LIP) were collected, forming an initial dataset. The acquisition of hyperspectral imagery utilized the Mjolnir VS-620 sensor during an aerial survey. Manufactured by HySpex, this advanced sensor captures wavelength information across 490 spectral bands, spanning from 400 nm to 2500 nm. Notably, it comprises 200 bands in the visible and near-infrared (VNIR) with a spectral interval of approximately 3.0 nm and 290 bands in the short-wave infrared (SWIR) spectrum with a spectral interval of about 5.1 nm [36]. Conducted over three days with a Cessna TU206F Turbo aircraft, the aerial survey covered the geographical expanse of Cartagena (80 km\({}^{2}\)). Following data acquisition, the obtained images underwent rigorous radiometric, geometric, and atmospheric corrections. Radiometric, geometric, and atmospheric corrections are essential preprocessing steps applied to hyperspectral imagery to ensure data accuracy and comparability across different datasets [37; 38]. Radiometric correction adjusts for sensor-specific responses and variations in illumination, thereby normalizing the pixel values to reflect true surface reflectance. Geometric correction aligns the imagery spatially, correcting distortions caused by sensor movement, platform instability, and the Earth's curvature, ensuring that the image accurately represents the Earth's surface. Atmospheric correction mitigates the effects of atmospheric particles, gases, and water vapor, which can distort the spectral signatures by scattering and absorbing radiation. Applying these corrections is crucial for obtaining reliable data that can be used for precise analysis and comparison in remote sensing studies. These corrective measures were applied using the HySpex RAD(r) (Version 1), Rese PARGE(r) (Version 1), and Rese DROACOR(r) (Version 2.0.1) software tools, respectively. The spectral information was filtered by removing bands within the atmospheric window that lacked relevant information, resulting in 380 useful bands from the 72 asbestos-cement tiles.