## 3 Results and Discussion
Both cases mentioned above refer to case studies in Italy, so the validity of the proposed approach remains to be confirmed in other contexts, such as tropical or sub-desert areas, where the vegetation on roofs and the type of deterioration may differ. In much of China, India, and Latin America, this difference could be significant. In fact, these countries are just beginning to ban asbestos, and in the next decade, there will likely be an increase in demand for remote sensing technologies for identifying AC roofs. Additionally, it is essential to highlight that these studies did not leverage the latest machine learning techniques, which constitute a central aspect of novelty in our work. Machine learning, a branch of artificial intelligence, offers several advantages, including the efficient analysis of large datasets in significantly shorter times than before, identifying patterns and trends that may be challenging to discover with traditional methods. In this case, the application of various supervised machine learning methods represents a step forward, demonstrating promising prediction performance and effective model generalization. In this context, it is relevant to note that most existing research focuses on the individual evaluation of a single machine learning method at a time only for the pure identification of AC roofs. These studies generally do not discriminate between the deteriorative states of AC roofs. This trend limits the comprehensive understanding of the relative effectiveness of various approaches. The comparison of machine learning methods for determining the deteriorative state of AC roofs is a significant innovation. The article presented contributes significantly to the scientific literature by adopting and expanding the use of advanced machine learning techniques for the identification of asbestos-cement roofs. The implementation of supervised classifiers, such as decision trees, KNN, logistic regression, support vector machine, and random forest, has often outperformed traditional rule-based methods and image analysis. This innovative approach highlights the versatility and effectiveness of machine learning methods in hazardous material management, specifically in identifying the deteriorative state of asbestos-cement roofs in urban environments. Despite the achievements, it is imperative to recognize the inherent limitations of employing machine learning in the construction and demolition domain. Factors such as the complex interdependencies between feature representation, classifier type, and hyper parameter tuning must be approached with caution. Additionally, the lack of labeled data to train and validate models remains a significant challenge, emphasizing the ongoing need to collect representative and comprehensive datasets. Future research could benefit from integrating more advanced machine learning techniques. For instance, deep learning approaches, such as those discussed in [44], could enhance segmentation processes and improve the overall accuracy of model predictions. Additionally, the handling of temporal data could be refined by exploring methods used in high-frequency trading, as detailed in [45]. Incorporating these advanced techniques could further strengthen the model's performance and expand its applicability in diverse environmental conditions and urban settings. In the context of our article, it is crucial to underline that the focus on the systematic comparison between various machine learning methods constitutes a gap in the current literature.