The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform
Deborah Ramires - One of the best experts on this subject based on the ideXlab platform.
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Method validation for the identification of Asbestos–Cement roofing
Applied Geomatics, 2012Co-Authors: Lorenza Fiumi, Stefano Casciardi, Antonella Campopiano, Deborah RamiresAbstract:The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping Asbestos–Cement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as Asbestos–Cement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of Asbestos–Cement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of Asbestos–Cement in the studied area has been also collected. The Asbestos–Cement surfaces of buildings vary from 100 to 5,000 m^2, totalling to 30,800 m^2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km^2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of Asbestos-Cement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.
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method validation for the identification of Asbestos Cement roofing
Applied Geomatics, 2012Co-Authors: Lorenza Fiumi, Stefano Casciardi, Antonella Campopiano, Deborah RamiresAbstract:The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping Asbestos–Cement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as Asbestos–Cement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of Asbestos–Cement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of Asbestos–Cement in the studied area has been also collected. The Asbestos–Cement surfaces of buildings vary from 100 to 5,000 m2, totalling to 30,800 m2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of Asbestos-Cement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.
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Risk assessment of the decay of Asbestos Cement roofs.
Annals of Occupational Hygiene, 2009Co-Authors: Antonella Campopiano, Deborah Ramires, Aneta Maria Zakrzewska, Rosa Ferri, Antonio D'annibale, Giancarlo PizzutelliAbstract:Objectives: In an assessment of the risk of Asbestos fibres release from Asbestos Cement materials, an important role is played by the assessment of the surface corrosion and by the disaggregation of Asbestos Cement. The aim of this work is to evaluate the differences among several methods used for the risk assessment that lead to a specific choice of abatement techniques. Methods: The state of deterioration of 40 Asbestos Cement roofs was evaluated using two priority assessment algorithms elaborated in Italy, the ‘pull-up test’ described by the Italian Organization for Standardization and the indicators described in the Italian legislation coupled with the observation of a small sample, taken from each roof, by a stereomicroscope. Results: The results obtained with the methods, proposed in this study, for the risk assessment of the decay of Asbestos Cement roofs show slight differences among them, only one deviates from the others in judgement on the state of conservation of the roof. Conclusions: It is very important to train the operator conducting the study since a completely subjectivity-free method does not exist. Whatever method is used will always be affected by the subjectivity linked to the competency and the training of the operator. Moreover, each method on its own cannot assess the risk of exposure to Asbestos, but reliable assessment of Asbestos-containing materials requires the use of more than one method, such as visual inspections, a pull-up test, and an assessment algorithm.
Antonella Campopiano - One of the best experts on this subject based on the ideXlab platform.
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Method validation for the identification of Asbestos–Cement roofing
Applied Geomatics, 2012Co-Authors: Lorenza Fiumi, Stefano Casciardi, Antonella Campopiano, Deborah RamiresAbstract:The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping Asbestos–Cement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as Asbestos–Cement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of Asbestos–Cement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of Asbestos–Cement in the studied area has been also collected. The Asbestos–Cement surfaces of buildings vary from 100 to 5,000 m^2, totalling to 30,800 m^2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km^2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of Asbestos-Cement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.
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method validation for the identification of Asbestos Cement roofing
Applied Geomatics, 2012Co-Authors: Lorenza Fiumi, Stefano Casciardi, Antonella Campopiano, Deborah RamiresAbstract:The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping Asbestos–Cement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as Asbestos–Cement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of Asbestos–Cement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of Asbestos–Cement in the studied area has been also collected. The Asbestos–Cement surfaces of buildings vary from 100 to 5,000 m2, totalling to 30,800 m2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of Asbestos-Cement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.
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Risk assessment of the decay of Asbestos Cement roofs.
Annals of Occupational Hygiene, 2009Co-Authors: Antonella Campopiano, Deborah Ramires, Aneta Maria Zakrzewska, Rosa Ferri, Antonio D'annibale, Giancarlo PizzutelliAbstract:Objectives: In an assessment of the risk of Asbestos fibres release from Asbestos Cement materials, an important role is played by the assessment of the surface corrosion and by the disaggregation of Asbestos Cement. The aim of this work is to evaluate the differences among several methods used for the risk assessment that lead to a specific choice of abatement techniques. Methods: The state of deterioration of 40 Asbestos Cement roofs was evaluated using two priority assessment algorithms elaborated in Italy, the ‘pull-up test’ described by the Italian Organization for Standardization and the indicators described in the Italian legislation coupled with the observation of a small sample, taken from each roof, by a stereomicroscope. Results: The results obtained with the methods, proposed in this study, for the risk assessment of the decay of Asbestos Cement roofs show slight differences among them, only one deviates from the others in judgement on the state of conservation of the roof. Conclusions: It is very important to train the operator conducting the study since a completely subjectivity-free method does not exist. Whatever method is used will always be affected by the subjectivity linked to the competency and the training of the operator. Moreover, each method on its own cannot assess the risk of exposure to Asbestos, but reliable assessment of Asbestos-containing materials requires the use of more than one method, such as visual inspections, a pull-up test, and an assessment algorithm.
Ewa Wilk - One of the best experts on this subject based on the ideXlab platform.
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Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)
Remote Sensing, 2020Co-Authors: Małgorzata Krówczyńska, Edwin Raczko, Natalia Staniszewska, Ewa WilkAbstract: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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Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm
Sustainability, 2019Co-Authors: Ewa Wilk, Małgorzata Krówczyńska, Bogdan ZagajewskiAbstract:The unique set of physical and chemical properties of Asbestos has led to its many industrial applications worldwide, of which roofing and facades constitute approximately 80% of currently used Asbestos-containing products. Since Asbestos-containing products are harmful to human health, their use and production have been banned in many countries. To date, no research has been undertaken to estimate the total amount of Asbestos–Cement products used at the country level in relation to regions or other administrative units. The objective of this paper is to present a possible new solution for developing the spatial distribution of Asbestos–Cement products used across the country by applying the supervised machine learning algorithm, i.e., Random Forest. Based on the results of a physical inventory taken on Asbestos–Cement products with the use of aerial imagery, and the application of selected features, considering the socio-economic situation of Poland, i.e., population, buildings, public finance, housing economy and municipal infrastructure, wages, salaries and social security benefits, agricultural census, entities of the national economy, labor market, environment protection, area of built-up surfaces, historical belonging to annexations, and data on Asbestos manufacturing plants, best Random Forest models were computed. The selection of important variables was made in the R v.3.1.0 program and supported by the Boruta algorithm. The prediction of the amount of Asbestos–Cement products used in communes was executed in the randomForest package. An algorithm explaining 75.85% of the variance was subsequently used to prepare the prediction map of the spatial distribution of the amount of Asbestos–Cement products used in Poland. The total amount was estimated at 710,278,645 m2 (7.8 million tons). Since the best model used data on built-up surfaces which are available for the whole of Europe, it is worth considering the use of the developed method in other European countries, as well as to assess the environmental risk of Asbestos exposure to humans.
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modelling the spatial distribution of Asbestos Cement products in poland with the use of the random forest algorithm
Sustainability, 2019Co-Authors: Ewa Wilk, Malgorzata Krowczynska, Bogdan ZagajewskiAbstract:The unique set of physical and chemical properties of Asbestos has led to its many industrial applications worldwide, of which roofing and facades constitute approximately 80% of currently used Asbestos-containing products. Since Asbestos-containing products are harmful to human health, their use and production have been banned in many countries. To date, no research has been undertaken to estimate the total amount of Asbestos–Cement products used at the country level in relation to regions or other administrative units. The objective of this paper is to present a possible new solution for developing the spatial distribution of Asbestos–Cement products used across the country by applying the supervised machine learning algorithm, i.e., Random Forest. Based on the results of a physical inventory taken on Asbestos–Cement products with the use of aerial imagery, and the application of selected features, considering the socio-economic situation of Poland, i.e., population, buildings, public finance, housing economy and municipal infrastructure, wages, salaries and social security benefits, agricultural census, entities of the national economy, labor market, environment protection, area of built-up surfaces, historical belonging to annexations, and data on Asbestos manufacturing plants, best Random Forest models were computed. The selection of important variables was made in the R v.3.1.0 program and supported by the Boruta algorithm. The prediction of the amount of Asbestos–Cement products used in communes was executed in the randomForest package. An algorithm explaining 75.85% of the variance was subsequently used to prepare the prediction map of the spatial distribution of the amount of Asbestos–Cement products used in Poland. The total amount was estimated at 710,278,645 m2 (7.8 million tons). Since the best model used data on built-up surfaces which are available for the whole of Europe, it is worth considering the use of the developed method in other European countries, as well as to assess the environmental risk of Asbestos exposure to humans.
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Estimation of the amount of Asbestos-Cement roofing in Poland.
Waste Management & Research, 2017Co-Authors: Ewa Wilk, Małgorzata Krówczyńska, Piotr Pabjanek, Piotr MedrzyckiAbstract:The unique set of physical and chemical properties has led to many industrial applications of Asbestos worldwide; one of them was roof covering. Asbestos is harmful to human health, and therefore its use was legally forbidden. Since in Poland there is no adequate data on the amount of Asbestos-Cement roofing, the objective of this study was to estimate its quantity on the basis of physical inventory taking with the use of aerial imagery, and the application of selected statistical features. Data pre-processing and analysis was executed in R Statistical Environment v. 3.1.0. Best random forest models were computed; model explaining 72.9% of the variance was subsequently used to prepare the prediction map of the amount of Asbestos-Cement roofing in Poland. Variables defining the number of farms, number and age of buildings, and regional differences were crucial for the analysis. The total amount of Asbestos roofing in Poland was estimated at 738,068,000 m2 (8.2m t). It is crucial for the landfill developmen...
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Mapping Asbestos-Cement roofing with the use of APEX hyperspectral airborne imagery: Karpacz area, Poland – a case study
Miscellanea geographica, 2016Co-Authors: Małgorzata Krówczyńska, Ewa Wilk, Piotr Pabjanek, Bogdan Zagajewski, Koen MeulemanAbstract:Abstract Asbestos and Asbestos containing products are harmful to human health, and therefore its use has been legally forbidden in the EU. Since there is no adequate data on the amount of Asbestos-Cement roofing in Poland, the objective of this study was to map Asbestos-Cement roofing with the use of hyperspectral APEX data (288 bands at the spatial resolution of 2.7 m) in the Karpacz area (southwest Poland). A field survey constituted the basis for training and verification polygons in the classification process. A SAM classification method was performed with the following classification results: 62% producer’s accuracy, 73% user’s accuracy and an overall accuracy of 95%. The Asbestos-Cement roofing for buildings may be discriminated with a high classification accuracy with the use of hyperspectral imagery. The vast majority of the classified buildings were characterised by their small area (i.e. residential type buildings), which reduced the overall accuracy of the classification.
Lorenza Fiumi - One of the best experts on this subject based on the ideXlab platform.
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Method validation for the identification of Asbestos–Cement roofing
Applied Geomatics, 2012Co-Authors: Lorenza Fiumi, Stefano Casciardi, Antonella Campopiano, Deborah RamiresAbstract:The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping Asbestos–Cement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as Asbestos–Cement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of Asbestos–Cement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of Asbestos–Cement in the studied area has been also collected. The Asbestos–Cement surfaces of buildings vary from 100 to 5,000 m^2, totalling to 30,800 m^2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km^2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of Asbestos-Cement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.
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method validation for the identification of Asbestos Cement roofing
Applied Geomatics, 2012Co-Authors: Lorenza Fiumi, Stefano Casciardi, Antonella Campopiano, Deborah RamiresAbstract:The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping Asbestos–Cement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as Asbestos–Cement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of Asbestos–Cement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of Asbestos–Cement in the studied area has been also collected. The Asbestos–Cement surfaces of buildings vary from 100 to 5,000 m2, totalling to 30,800 m2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of Asbestos-Cement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.
Bogdan Zagajewski - One of the best experts on this subject based on the ideXlab platform.
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modelling the spatial distribution of Asbestos Cement products in poland with the use of the random forest algorithm
Sustainability, 2019Co-Authors: Ewa Wilk, Malgorzata Krowczynska, Bogdan ZagajewskiAbstract:The unique set of physical and chemical properties of Asbestos has led to its many industrial applications worldwide, of which roofing and facades constitute approximately 80% of currently used Asbestos-containing products. Since Asbestos-containing products are harmful to human health, their use and production have been banned in many countries. To date, no research has been undertaken to estimate the total amount of Asbestos–Cement products used at the country level in relation to regions or other administrative units. The objective of this paper is to present a possible new solution for developing the spatial distribution of Asbestos–Cement products used across the country by applying the supervised machine learning algorithm, i.e., Random Forest. Based on the results of a physical inventory taken on Asbestos–Cement products with the use of aerial imagery, and the application of selected features, considering the socio-economic situation of Poland, i.e., population, buildings, public finance, housing economy and municipal infrastructure, wages, salaries and social security benefits, agricultural census, entities of the national economy, labor market, environment protection, area of built-up surfaces, historical belonging to annexations, and data on Asbestos manufacturing plants, best Random Forest models were computed. The selection of important variables was made in the R v.3.1.0 program and supported by the Boruta algorithm. The prediction of the amount of Asbestos–Cement products used in communes was executed in the randomForest package. An algorithm explaining 75.85% of the variance was subsequently used to prepare the prediction map of the spatial distribution of the amount of Asbestos–Cement products used in Poland. The total amount was estimated at 710,278,645 m2 (7.8 million tons). Since the best model used data on built-up surfaces which are available for the whole of Europe, it is worth considering the use of the developed method in other European countries, as well as to assess the environmental risk of Asbestos exposure to humans.
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Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm
Sustainability, 2019Co-Authors: Ewa Wilk, Małgorzata Krówczyńska, Bogdan ZagajewskiAbstract:The unique set of physical and chemical properties of Asbestos has led to its many industrial applications worldwide, of which roofing and facades constitute approximately 80% of currently used Asbestos-containing products. Since Asbestos-containing products are harmful to human health, their use and production have been banned in many countries. To date, no research has been undertaken to estimate the total amount of Asbestos–Cement products used at the country level in relation to regions or other administrative units. The objective of this paper is to present a possible new solution for developing the spatial distribution of Asbestos–Cement products used across the country by applying the supervised machine learning algorithm, i.e., Random Forest. Based on the results of a physical inventory taken on Asbestos–Cement products with the use of aerial imagery, and the application of selected features, considering the socio-economic situation of Poland, i.e., population, buildings, public finance, housing economy and municipal infrastructure, wages, salaries and social security benefits, agricultural census, entities of the national economy, labor market, environment protection, area of built-up surfaces, historical belonging to annexations, and data on Asbestos manufacturing plants, best Random Forest models were computed. The selection of important variables was made in the R v.3.1.0 program and supported by the Boruta algorithm. The prediction of the amount of Asbestos–Cement products used in communes was executed in the randomForest package. An algorithm explaining 75.85% of the variance was subsequently used to prepare the prediction map of the spatial distribution of the amount of Asbestos–Cement products used in Poland. The total amount was estimated at 710,278,645 m2 (7.8 million tons). Since the best model used data on built-up surfaces which are available for the whole of Europe, it is worth considering the use of the developed method in other European countries, as well as to assess the environmental risk of Asbestos exposure to humans.
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Mapping Asbestos-Cement roofing with the use of APEX hyperspectral airborne imagery: Karpacz area, Poland – a case study
Miscellanea geographica, 2016Co-Authors: Małgorzata Krówczyńska, Ewa Wilk, Piotr Pabjanek, Bogdan Zagajewski, Koen MeulemanAbstract:Abstract Asbestos and Asbestos containing products are harmful to human health, and therefore its use has been legally forbidden in the EU. Since there is no adequate data on the amount of Asbestos-Cement roofing in Poland, the objective of this study was to map Asbestos-Cement roofing with the use of hyperspectral APEX data (288 bands at the spatial resolution of 2.7 m) in the Karpacz area (southwest Poland). A field survey constituted the basis for training and verification polygons in the classification process. A SAM classification method was performed with the following classification results: 62% producer’s accuracy, 73% user’s accuracy and an overall accuracy of 95%. The Asbestos-Cement roofing for buildings may be discriminated with a high classification accuracy with the use of hyperspectral imagery. The vast majority of the classified buildings were characterised by their small area (i.e. residential type buildings), which reduced the overall accuracy of the classification.
