Machine Learning-Based Classification of Asbestos-Containing Roofs Using Airborne RGB and Thermal Imagery

Gordana Kaplan (), Mateo Gašparović, Onur Kaplan, Vancho Adjiski, Resul Comert and Mohammad Asef Mobariz
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Gordana Kaplan: Institute of Earth and Space Sciences, Eskisehir Technical University, Eskisehir 26555, Turkey
Mateo Gašparović: Chair of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia
Onur Kaplan: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Vancho Adjiski: Faculty of Natural and Technical Sciences, Goce Delchev University, 2000 Stip, North Macedonia
Resul Comert: Institute of Earth and Space Sciences, Eskisehir Technical University, Eskisehir 26555, Turkey
Mohammad Asef Mobariz: Institute of Graduate School, Eskisehir Technical University, Eskisehir 26555, Turkey

Sustainability, 2023, vol. 15, issue 7, 1-16

Abstract: Detecting asbestos-containing roofs has been of great interest in the past few years as the substance negatively affects human health and the environment. Different remote sensing data have been successfully used for this purpose. However, RGB and thermal data have yet to be investigated. This study aims to investigate the classification of asbestos-containing roofs using RGB and airborne thermal data and state-of-the-art machine learning (ML) classification techniques. With the rapid development of ML reflected in this study, we evaluate three classifiers: Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). We have used several image enhancement techniques to produce additional bands to improve the classification results. For feature selection, we used the Boruta technique; based on the results, we have constructed four different variations of the dataset. The results showed that the most important features for asbestos-containing roof detection were the investigated spectral indices in this study. From a ML point of view, SVM outperformed RF and XGBoost in the dataset using only the spectral indices, with a balanced accuracy of 0.93. Our results showed that RGB bands could produce as accurate results as the multispectral and hyperspectral data with the addition of spectral indices.

Keywords: remote sensing; GIS; machine learning; asbestos; roofs; buildings; Google Street View (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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