Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/151692
Title: Explainable automatic detection of fiber-cement roofs in aerial RGB images
Author: Omarzadeh, Davoud  
González Godoy, Adonis  
Bustos, Cristina  
Martín-Fernández, Kevin
Scotto, Carles
Sánchez, César
Lapedriza, Agata  
Borge-Holthoefer, Javier  
Citation: Omarzadeh, D. [Davoud], Gonzalez Godoy, A.R. [Adonis Rafael], Bustos, C. [Cristina], Martin Fernandez, K. [Kevin], Scotto, C. [Carles], Sánchez, C. [Cesar], Lapedriza, A. [Agata] & Borge-Holthoefer, J. [Javier]. (2024). Explainable automatic detection of fiber-cement roofs in aerial RGB images. Remote Sensing, 16(8), 1-23. doi: 10.3390/rs16081342
Abstract: Following European directives, asbestos–cement corrugated roofing tiles must be eliminated by 2025. Therefore, identifying asbestos–cement rooftops is the first necessary step to proceed with their removal. Unfortunately, asbestos detection is a challenging task. Current procedures for identifying asbestos require human exploration, which is costly and slow. This has motivated the interest of governments and companies in developing automatic tools that can help to detect and classify these types of materials that are dangerous to the population. This paper explores multiple computer vision techniques based on Deep Learning to advance the automatic detection of asbestos in aerial images. On the one hand, we trained and tested two classification architectures, obtaining high accuracy levels. On the other, we implemented an explainable AI method to discern what information in an RGB image is relevant for a successful classification, ensuring that our classifiers’ learning process is guided by the right variables—color, surface patterns, texture, etc.—observable on asbestos rooftops.
Keywords: asbestos
aerial imagery
deep learning
explainable AI
public health
DOI: https://doi.org/10.3390/rs16081342
Document type: info:eu-repo/semantics/article
Version: info:eu-repo/semantics/publishedVersion
Issue Date: 11-Apr-2024
Publication license: http://creativecommons.org/licenses/by/3.0/es/  
Linked data: https://mdpi.altmetric.com/details/163037239
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Articles cientÍfics

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