Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/3/408 |
| _version_ | 1818958555152121856 |
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| author | Małgorzata Krówczyńska Edwin Raczko Natalia Staniszewska Ewa Wilk |
| author_facet | Małgorzata Krówczyńska Edwin Raczko Natalia Staniszewska Ewa Wilk |
| author_sort | Małgorzata Krówczyńska |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T11:27:36Z |
| format | Article |
| id | doaj.art-7c537773d58f46578f74546d67edbe0b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:27:36Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7c537773d58f46578f74546d67edbe0b2022-12-21T19:42:19ZengMDPI AGRemote Sensing2072-42922020-01-0112340810.3390/rs12030408rs12030408Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)Małgorzata Krówczyńska0Edwin Raczko1Natalia Staniszewska2Ewa Wilk3Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, PolandDepartment of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, PolandIndependent Researcher, 00-927 Warsaw, PolandDepartment of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, PolandDue 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.https://www.mdpi.com/2072-4292/12/3/408convolutional neural networksasbestos identificationimage recognitionremote sensing |
| spellingShingle | Małgorzata Krówczyńska Edwin Raczko Natalia Staniszewska Ewa Wilk Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs) Remote Sensing convolutional neural networks asbestos identification image recognition remote sensing |
| title | Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs) |
| title_full | Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs) |
| title_fullStr | Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs) |
| title_full_unstemmed | Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs) |
| title_short | Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs) |
| title_sort | asbestos cement roofing identification using remote sensing and convolutional neural networks cnns |
| topic | convolutional neural networks asbestos identification image recognition remote sensing |
| url | https://www.mdpi.com/2072-4292/12/3/408 |
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