Mapping Asbestos-Cement Roofing with Hyperspectral  Remote Sensing over a Large Mountain Region of the Italian Western Alps

Frassy, Federico; Candiani, Gabriele; Rusmini, Marco; Maianti, Pieralberto; Marchesi, Andrea; Nodari, Francesco Rota; Via, G. Dalla; Albonico, Carlo; Gianinetto, Marco

doi:10.3390/s140915900

Cited by 46 publications

(46 citation statements)

References 21 publications

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“…However, the accuracy will increase as the average roof area increases [39]. Asbestos roofing mapping results in larger areas, i.e., 3263 km 2 [20] and 795 km 2 [42], were characterized by significantly lower accuracies of 43% and 65% respectively. The underestimation of the class was 48% and 65%.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

“…Frassy et al [20] have pointed out in research that the minimum identifiable roof area should be at minimum the size of 3 by 3 pixels. This assumption sets the minimum roof area eligible for identification to over 100 m 2 , for MIVIS, Aisa Eagle II, APEX, QuickBird, and WorldView-2 images.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…The SAM (Spectral Angle Mapper) classification algorithm [17] was applied to the selected MNF components based on promising results obtained in previous studies [8,10]. The advantage of using this algorithm is that it is not heavily affected by residual variations in scene illumination after correction.…”

Section: Classification Procedures and Accuracy Evaluationmentioning

confidence: 99%

“…Remote sensing can help in overcoming these omissions by providing maps of AC used as roofing material. Aerial remote sensing techniques have been successfully applied in the recent past for asbestos mapping purposes [7][8][9][10][11]. To the best of our knowledge, only one study [12] deals with the assessment of AC deterioration through spectral diagnostic bands.…”

Section: Introductionmentioning

confidence: 99%

Mapping of Asbestos Cement Roofs and Their Weathering Status Using Hyperspectral Aerial Images

Cilia

Panigada

Rossini

et al. 2015

IJGI

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Abstract:The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer's accuracy (PA) of 86% and a user's accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the "Index of Surface Deterioration" (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status.

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“…The determination of the amount of asbestos-containing materials is essential for the landfill development program and the application of adequate policies and monitoring systems. The estimation of the amount asbestos-cement products is raised; in Italy by Fiumi et al [8,9], Frassy et al [10], in Hungary by Szabó et al [27] and in Poland by Krów-czyńska et al [18].…”

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confidence: 99%

Hyperspectral discrimination of asbestos‑cement roofing

Krówczyńska

Wilk

Pabjanek

et al. 2017

geom

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Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps

Cited by 46 publications

References 21 publications

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

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

Mapping of Asbestos Cement Roofs and Their Weathering Status Using Hyperspectral Aerial Images

Hyperspectral discrimination of asbestos‑cement roofing

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