In this study, an innovative approach was developed to assess the condition of asbestoscement (AC) roofs by integrating field data and utilizing five supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. 

The method includes evaluating the importance of 380 reflectance bandsto identify key indicators of AC roof deterioration. The study efficiently organizes and prioritizes relevant reflectance bands using the open-source software tool Weka, specifically the InfoGainAttributeEval method and Ranker search method. The research not only contributes methodologically but also serves as a resource for streamlined asbestos tile inspections, especially beneficial for developing nations with nascent asbestos regulations. 

The ultimate goal is to address health concerns related to asbestos exposure, emphasizing public health and environmental sustainability within the CRISP-DM framework.