Journal Article

Explainable Automatic Detection of Fiber–Cement Roofs in Aerial RGB Images

Scotto, Carles, Omarzadeh, Davoud, Sánchez, César, Lapedriza, Agata, Borge-Holthoefer, Javier, Martín-Fernández, Kevin, González-Godoy, Adonis, Bustos, Cristina

2024

Overview

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.

• Publisher: MDPI AG
• Subject: Accuracy / aerial imagery / Algorithms / Artificial intelligence / Asbestos / automatic detection / Buildings / Cement / Classification / Color imagery / Computer vision / Councils / Datasets / Deep learning / Environmental aspects / explainable AI / Explainable artificial intelligence / humans / Identification / Image processing / Machine learning / Machine vision / Materials / Methods / Neural networks / public health / Remote sensing systems / Roofing / Roofs / Surface layers / texture