###### Abstract  
This study introduces an innovative approach to evaluate the condition of asbestos-cement (AC) roofs by integrating field data with five distinct supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. A novel methodology for assessing the importance of 380 reflectance bands was employed, offering fresh insights into the key indicators of AC roof deterioration. The research systematically organized and prioritized reflectance bands based on their information gain, optimizing both the selection of relevant bands and the performance of the models in differentiating between low and high intervention priority (LIP and HIP) roofs. The decision tree model, when applied to the top 10 most relevant bands, achieved the highest cross-validation accuracy of 76.047%, making it the most effective tool for identifying AC roof conditions. Additionally, the random forest model demonstrated strong performance across various band groups, further validating its utility. Utilizing the open-source software Weka (version 3.8.6), this study adeptively executed relevance evaluation and model implementation, providing a practical and scalable solution for material characterization, especially in regions where resources for spectral and hyperspectral image analysis are limited. The findings of this study offer valuable tools for government and environmental authorities, particularly in developing countries, where efficient and cost-effective AC roof assessment is crucial for public health and safety. The methodology is adaptable to different urban environments and climatic conditions, supporting global efforts in asbestos management, especially in countries where asbestos regulations are newly implemented. Organized within the CRISP-DM framework, this paper details the methodological phases, presents compelling results on reflectance band relevance, evaluates machine learning models, and concludes with prospects for future research aimed at enhancing asbestos detection and removal strategies. a +
Footnote †: _Applied_  
domains, prominently feature asbestos in roofing, walls, and floors. Nonetheless, the recognition of its detrimental health impacts, encompassing conditions such as lung cancer, mesothelioma, and asbestos, became pronounced in the early 1900s [4; 5; 6; 7; 8]. Transformative in environmental monitoring, remote sensing technologies provide a robust approach for discerning and analyzing the spatial distribution of materials, particularly in domains such as agriculture and geological exploration [9; 10; 11]. Notably, the potential of remote sensing is significant in the detection of asbestos-cement (AC) roofs [12; 13; 11]. The utilization of spectral sensors on unmanned or manned aircraft, as well as satellites, necessitates a trade-off between spatial resolution (SRE) and cost. Unmanned aircraft present high SRE but encounter logistical challenges hindering large-scale imaging. Manned aircraft strike a balance, albeit being more prevalent in developed countries. Technological advancements have propelled satellite imagery, making it cost-effective with competitive SRE in the short-wave infrared (SWIR) and visible/near-infrared (VNIR) bands, rendering it appealing without the need for flight permits [11]. In the realm of asbestos-cement (AC) identification, computational algorithms play a pivotal role, with methods such as K-Nearest Neighbors (KNN) [14], object-based image analysis (OBIA) [15], Spectral Feature Fitting (SFF) [16], Spectral Angle Mapper (SAM) [17], support vector machine (SVM) [18], decision trees, random forest (RF), discriminant function analysis (DFA) [12], maximum likelihood method (MLC), and Convolutional Neural Networks (CNNs) [19]. Surprisingly, there exists a dearth of comparative research on the efficacy of machine learning methodologies for AC identification.