###### Abstract
Noteworthy studies in the literature have demonstrated successful AC roof classification using hyperspectral imagery [20; 21; 22], encompassing both conventional [12] and advanced methodologies [20; 23]. Limited attention has been directed towards examining the degradation state of asbestos-cement (AC) roofs. [PERSON] et al. (2015) [21] propose an index based on spectral responses to assess AC roof deterioration, validated through field measurements. However, the generalizability of this index across diverse latitudes, particularly in tropical and desert urban areas, remains unverified. Additionally, there is a paucity of investigations utilizing hyperspectral images in this domain. Another study explores the remote sensing assessment of asbestos ceiling degradation [24], establishing correlations between quantified mineral fibers and spectral reactivity in the short-wave infrared (SWIR) and thermal infrared (TIR) regions. Despite encouraging outcomes, the study is constrained by limited field observations and the use of multispectral aerial images with insufficient 3.0 m ground accuracy. A significant research gap persists in the assessment of asbestos roof degradation and the establishment of benchmarks for prioritizing interventions. Although machine learning methodologies show promise in this area, their application remains relatively un-derexplored, particularly when combined with open-access tools. The use of hyperspectral imagery is similarly underutilized, especially in tropical and arid urban regions where climatic factors could influence outcomes. This issue is of global importance, particularly in developing nations like Colombia, which have recently implemented asbestos bans [25] and urgently require effective methods for environmental and public health mitigation. This study focuses on Cartagena de Indias, a UNESCO cultural heritage city in northern Colombia that faces environmental challenges due to widespread asbestos use. It is crucial to acknowledge the limitations in the current body of knowledge regarding asbestos-cement (AC) roof assessment. While remote sensing and machine learning techniques have advanced the identification of AC materials, there remains a dearth of studies that thoroughly examine the condition and degradation state of these roofs, particularly in tropical and arid urban contexts. Additionally, the integration of hyperspectral data with advanced machine learning models for this purpose remains insufficiently explored, particularly in developing countries where the public health implications are most severe. This study addresses these gaps by providing a comparative analysis of multiple machine learning algorithms and introducing a novel application of information gain-based feature selection for hyperspectral bands. By focusing on Cartagena de Indias, this research not only enhances the technical methodology but also offers a crucial framework for environmental and health interventions in regions where asbestos regulations are newly emerging. This work aims to propose and validate a methodology for identifying the condition of asbestos-cement (AC) roofs by comparing field data with the performance of five supervised learning models (decision trees (DT), KNN, logistic regression, support vector machine, and random forest). The evaluation utilized the cross-validation method with five folds and the confusion matrix [26], along with associated metrics. Relevance assessment of the 380 reflectance bands employed information gain-based methods. The resulting ranking of relevant bands, obtained through these methods and a merit metric, was used to group bands and assess the optimal number of bands and the model with the best performance in distinguishing between asbestos tiles with low intervention priority (LIP) or high intervention priority (HIP). Both relevance evaluation and model implementation utilized functionalities of the open-source software tool Weka, employing the InfoGainAttributeEval method and Ranker search method for relevance assessment.