## 2 Materials and Methods
Subsequently, in the data preparation phase, the process started first with the normalization of the reflectance bands in the dimensionless range [0, 1], and secondly with the use of synthetic data to balance the number of instances corresponding to asbestos-cement (AC) roofs with low intervention priority and those with high intervention priority. To accomplish this, and considering that hyperspectral signatures enable the differentiation of various material types based on the distinct peaks and variations in reflectance across different spectral bands, the characteristic spectral signature of roofs with LIP was first determined by averaging the reflectance values across the 380 spectral bands considered. Subsequently, to generate the 24 synthetic spectral signatures, 12 random numbers between 0 and 0.05 were selected, which were then subtracted from and added to each spectral band of the characteristic spectral signature of roofs with LIP. As a result, 12 of the generated signatures are positioned above the characteristic spectral signature, while 12 are positioned below it. This method ensured that the 24 synthetic signatures of roofs in good condition closely follow the characteristic spectral signature on a band-by-band basis. Consequently, a dataset comprising 96 AC roofs (48 with LIP and 48 with HIP) was generated. Although a dataset with 96 records might be considered insufficient for training a predictive model, it is important to highlight that each record comprises a total of 380 hyperspectral bands. These bands enable the detection of various materials based on the peaks and variations in reflectance and, as demonstrated in this research, allow for the differentiation of tiles made from the same material but in different conditions, such as roofs in good or poor condition. In Figure 2, the 24 signatures inserted into the initial dataset are presented, each including a minimally representative fixed value above and below the mean. Thus, a dataset with 380 columns corresponding to the data of 96 AC roofs and an additional column indicating the roof type (1 for AC roof with LIP and 0 for AC roof with HIP) was formed. In the normalization process of the reflectance bands, a dimensional normalization method was employed to adjust the reflectance values to a unitless range of [0, 1] (see Equation (1)). This technique was implemented using the NumPy library in Python, which provides efficient tools for handling large data arrays and performing mathematical operations [39; 40]. Dimensional normalization was chosen for its ability to standardize values without altering the original data distribution, thereby facilitating interpretation and subsequent analysis. Alternatives such as z-score normalization and rank normalization were considered, but dimensional normalization was preferred for its simplicity and lower risk of introducing artificial variability into the data. The choice of normalization method is crucial, as proper normalization can significantly impact the accuracy of classification models and the results of spectral analysis [41]. \[\text{Normalized value}=\frac{(\text{Real value}\ -\ \text{Minimum value})}{(\text{Maximum value}\ -\ \text{Minimum value})} \tag{1}\]  
Similarly, in the modeling and evaluation phase, the information gain-based method called InfoGainAttributeEval, along with the Ranker search method, was applied using the open-source software tool Weka. This aimed at identifying the ranking of reflectance bands influencing the detection of asbestos-cement (AC) roofs with LIP or HIP. Different band groups were formed based on the reflectance band ranking (from 10 bands to the total number of bands with an increment of 20 bands) to assess the performance of 5 supervised learning models (decision trees, KNN, logistic regression, support vector machine, and random forest) in each band group.