## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{**Type of**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Ground Resolution**} & \multirow{2}{*}{**Methodology**} & \multicolumn{2}{c}{**Deterioration**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Ref.**} \\  & \multicolumn{1}{c}{**Sensor/Satellite**} & & & & & & \\ \hline Italy & MIVIS & 102 & 3.0–4.0 m & Spectral Angle Mapper (SAM) & Yes & 2007 & [24] \\ \hline Brazil & \begin{tabular}{c} WorldView-2 \\ and QuickBird-2 \\ \end{tabular} & 3 and 3 & 0.5 m & Object-Based & No & 2011 & [46] \\ \hline Italy & MIVIS & 102 & 4.0 m & SAM & No & 2012 & [47] \\ \hline India & QuickBird & 3 & \begin{tabular}{c} Panchromatic = \\ 0.61–072 m; VNIR = 2.44–2.88 m \\ \end{tabular} & PCA-based; line-detection-based & No & 2012 & [48] \\ \hline Italy & Airborne & 102 & 3.0 m & SAM & No & 2014 & [12] \\ \hline Hungary & Hyperspectral & 126 & 0.15 m & \begin{tabular}{c} SAM, support vector \\ machine (SVM), and \\ Maximum likelihood \\ \end{tabular} & No & 2014 & [49] \\ \hline Italy & Airborne & 102 & 3.0 m & SAM & Yes & 2015 & [21] \\ \hline Spain & Hyperspectral & 32 & 2.0–2.4 m & \begin{tabular}{c} Integration of rooftop \\ greenhouses \\ \end{tabular} & No & 2017 & [50] \\ \hline Italy & MIVIS & 102 & 3.0 m & SAM & No & 2018 & [51] \\ \hline Hungary & WorldView-2 & 8 & \begin{tabular}{c} Panchromatic = 2 m \\ VNIR = 0.5 m \\ \end{tabular} & \begin{tabular}{c} LDFA = Linear \\ Discriminant Function \\ Analysis; QPEA = \\ Quadratic Discriminant \\ Function Analysis; RF = \\ \end{tabular} & No & 2018 & [52] \\ \hline Poland & NA & NA & NA & RF & No & 2019 & [53] \\ \hline Italy & WorldView-3 & 16 & \begin{tabular}{c} Panchromatic = 0.31 \\ m; VNIR = 1.24 m; SWIR = 3.70 m \\ \end{tabular} & QGIS Plugin named & No & 2019 & [54] \\ \hline Poland & Orthophotomap & 3 & 0.25 m & \begin{tabular}{c} Convolutional Neural \\ Networks (CNNs) \\ \end{tabular} & No & 2020 & [20] \\ \hline Brazil & WorldView-3 & 16 & \begin{tabular}{c} Panchromatic = 0.31 \\ m; VNIR = 1.24 m \\ \end{tabular} & \begin{tabular}{c} Maximum likelihood, \\ Mahalanobis Distance, \\ and Minimum Distance. \\ \end{tabular} & No & 2020 & [55] \\ \hline Taiwan & \begin{tabular}{c} Sentinel 2 and \\ airborne \\ Hyperspectral \\ \end{tabular} & 13 and 12 & \begin{tabular}{c} 10–60 cm and \\ unknown \\ \end{tabular} & CNNs and SVM & No & 2022 & [56] \\ \hline Poland & Orthophotomap & 3 & 0.25 m & CNNs & No & 2022 & [23] \\ \hline South Korea & Orthophotomap & NA & NA & Visual Counting Method & No & 2022 & [57] \\ \hline Colombia & \
\begin{tabular}{c} ClopNic \\ (Present Study) \\ \end{tabular} & Airborne & 490 & 0.8 m &
\begin{tabular}{c} DT, KNN, logistic \\ regression, SVM, and RF \\ \end{tabular} & Yes & 2024 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Literature review on remote sensing related to asbestos-cement roof identification by year. Similarly, this study aimed to disseminate at an academic level the advantages of open-source software tools such as Weka, both in the application of relevant attribute detection methods and in the application and comparison of different supervised learning methods, using various precision metrics. In this regard, the InfoGainAttributeEval and Ranker methods were employed to identify bands that provide greater information gain in detecting the condition of AC roofs. Likewise, five supervised learning methods were compared using cross-validation and metrics derived from the confusion matrix. The results obtained lead to the conclusion that Weka is a suitable alternative for conducting material detection studies on datasets derived from hyperspectral images. Furthermore, this work is particularly advantageous for developing countries due to its focus on cost-effective and accessible methods for assessing asbestos-cement (AC) roof conditions. In many developing nations, the resources available for large-scale environmental monitoring and public health interventions are limited.