Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)

Abstract

Due to the pathogenic nature of asbestos, a statutory ban on asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of asbestos–cement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of asbestos–cement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of asbestos–cement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. Asbestos–cement roofing products were classified with the producer’s accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of asbestos–cement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of asbestos–cement products in use is important for assessing environmental exposure to asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats.

A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories

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Malignant mesothelioma and asbestos exposure in Europe: Evidence of spatial clustering

“…It was pointed out that similar studies are needed in other countries to estimate the ongoing environmental and occupational MM risks worldwide, including the contribution of chrysotile. The possible solution to gauge the asbestos environmental exposure would be to estimate the geographical distribution of asbestos products still in use (Krówczyńska et al, 2020).…”

Machine Learning in Hazardous Building Material Management: Research Status and Applications

“…The macro-level involves detection and estimation of regional ACMs via image recognition in remote sensing. By compiling the aerial photographs, hyperspectral data, and multispectral imagery, Krówczyńska et al [13] built a hybrid database for modeling the quantity and location of asbestos-cement roofing. In their findings, the convolutional neural networks exhibited an overall high accuracy in classifying nature color images and color-infrared imagery of asbestos-cement roofing from other roof materials.…”

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