A QGIS Tool for Automatically Identifying Asbestos Roofing
2. Materials and Methods
2.1. Roofing Classification
2.1.1. Image Selection and Preprocessing
- Orthophoto: aerial photographs or images that have been geometrically corrected and georeferenced (“orthorectified”), with radiometric resolution in four spectral bands (R,G,B, NIR) and with a spectral coverage of up to ~800 nm. Such images are available free of charge at the Geoscope Observatory in Tuscany (http://www502.regione.toscana.it/geoscopio/ortofoto.html). Their low spectral resolution does not make it possible for their use in any asbestos classification.
- Vector graphics files, available at the Geoscope Observatory in Tuscany. Once the Geoscope website is accessed, the layer with the information requested can be chosen; for instance, surveyor maps.
- High spectral resolution images, acquired by airborne sensors (MIVIS) . They are very expensive if there are specific acquisition procedures. Their broad representative potential is limited by the noise occurring in high-depth bands.
- Satellite images acquired by the WorldView-3 sensor working on eight spectral bands. WorldView satellite imagery is often more expensive than RGB aerial imagery if a comparison is made between the archives. In the presented case, no archive aerial imagery is available, and additionally, WorldView images of the area of interest can be purchased at fairly low prices (about 2200 euros) .
- Pansharpening, the process that enabled a merger of the information collected from satellite imaging, as acquired by the WorldView-3 sensor, in order to create a single image with the resolution of the initial panchromatic image (Figure 2).The low-resolution image was interpolated with bi-cubic interpolation. In this activity, pansharpening was carried out using a component substitution algorithm. The, RCS (relative component substitution) algorithm was selected among those available, through Orfeo Tool-box , a software integrated into QGIS.Table 1 describes the features of the images used for such processing:
These images were georeferenced in UTM33 WGS84 and orthorectified. They were also calibrated and corrected radiometrically. Our research started in 2016, and it aimed to document the diffusion of asbestos roofs in the Prato area in 2014. For this reason, the selected images were among those acquired in 2014. In fact, from that moment onwards, the authorities of Prato have updated their database by inputting the removal procedures of asbestos-contaminated artifacts by individuals and companies.
- A panchromatic image with a higher spatial resolution (50 cm);
- A multispectral image having eight spectral bands (coastal, blue, green, yellow, red, red edge, NIR-1, NIR-2) and characterized by a spatial resolution of 2 m.
- Raster image filtering, meaning filtering of the pixel grid, was obtained through a vector mask of the chosen area. The vector mask was something that ensued from the cadastral shape layer, namely, from the surveyor map where all of the buildings that were related to the selected area were described via a vector graphics editor (Figure 3). The inferred mask only contained information that related to the buildings’ roofing. This process enabled the removal of everything but the buildings’ roofing from the initial satellite image (Figure 4). The cropping operation was performed using the corresponding functions offered by QGIS and based on the Gdal libraries.
- Partition of the filtered image in tiles, namely sections that had suitable dimensions, so as to carry out the classification as quickly as possible. The partition of the satellite image was carried out with a plugin called GridSplitter, thus obtaining 49 sections, each of them correctly geo-referenced according to the projection of the original image. Such tiles were saved into a single folder containing only and exclusively these files.
2.1.2. Classification Procedure
2.2. The RoofClassify Plugin
2.2.1. Plugin User Interface (UI)
- The image to be classified, whether it is single or divided into tiles, kept together in a single folder;
- A folder containing the shapes corresponding to each identified class;
- The image to be used as a training set, where different shapes are represented.
- Select raster(s) to be classified (folder): by selecting the ‘browse’ button, it is possible to search the folder that has the image (or images) to be classified.
- Select training shapes (folder): by selecting the ‘browse’ button, it is possible to search the folder that has shapefiles corresponding to the different classes of the training set.
- Select training raster (.tif): by selecting the ‘browse’ button, it is possible to search the geo-referenced image (filename extension ‘.tif’) whose pixels conform to the shapefiles set out in point 2.
- Create shape count: by ticking this (which is optional), the plugin creates not only a classified image, but also a shapefile (*.dbf) containing all of the polygons that correspond to each cladding. In this shape, each polygon (building) is provided with the following information: the identification code, the total number of pixels that each polygon has, and the pixel number of each identified class in the polygon.
- Create shape percentage: by ticking this (which is optional), the plugin creates not only a classified image, but also a shapefile (*.dbf) containing all of the polygons that correspond to each cladding. In this shape, each polygon (building) is provided with the following information: the identification code, and the percentage of classified pixels within each polygon for each identified class in that polygon.
- Select the file (.shp) to create a shape count/percentage: whether one of the two previously mentioned options or both of them have been ticked, this box makes it possible to select the shapefile to be used when calculating the required statistics. Generally speaking, this shapefile is the same as the one that is used in the filtering phase of the satellite image.
- Select the output folder: when clicking on ‘browse’, it is possible to select the folder where the output files will be saved.
2.2.2. Focusing on How the Classification Plugin is Organized
- Importing the raster image to be classified (images available in the selected folder as input dataset) into a Python data structure (multi-array), where each and every pixel matches exactly with a cell of the structure, made of eight components (one for each band).
- Importing the raster image, which represents the selected training set for classification purposes (same as point 1).
- Extracting the training set from the shapefile: a shapefile is defined for each class to be identified, and it includes the polygons matched with the related roofing. As previously explained, each and every shape (one shape matching with the asbestos class, one shape for a red roof, and so forth) is saved in a single folder. The algorithm will recursively access this folder, looking for the shapes in order to extract the training set. These data are also imported into a Python multiarray.
- Training the classifier file: the training and classification algorithm belongs to the “Scikit-learn” Python library. The multiclass “RandomForest” classification function was used in this case, which was more suitable for execution speed. The inputs were: the multiarray raster of the training set (point 2); the multiarray shapes of the training set (point 3).
- Classification according to the selected function. The output will be a multiarray comprising the classified pixels’ values.
- Reconstructing and saving the correctly geo-referenced image as the starting image for use, beginning with the output provided in point 5.
3. Classification Results and Validation
- Identifying the building roofing with well-known construction materials as well as their tagging according to the corresponding class.
- Creating a shapefile for each class, which included the previously tagged roofing types for each file, according to a specific class.
- Developing a Python script. Provided with input data, which consisted of both the image created from the classification process, and the shapefiles, as described in point 2, this script is capable of providing a confusion matrix as an output.
- Analyzing the confusion matrix and estimating any possible classification errors.
Conflicts of Interest
- Bartrip, P.W.J. History of asbestos related disease. Postgrad. Med. J. 2004, 80, 72–76. Available online: https://pmj.bmj.com/content/postgradmedj/80/940/72.full.pdf (accessed on 1 February 2019).[Green Version]
- Suzuki, Y.; Yuen, S.R.; Ashley, R. Short, thin asbestos fibers contribute to the development of human malignant mesothelioma: pathological evidence. Int. J. Hyg. Environ. Healt 2005, 208, 201–210. [Google Scholar] [CrossRef] [PubMed]
- Szabó, S.; Burai, P.; Kovács, Z.; Szabó, G.; Kerényi, A.; Fazekas, I.; Paládi, M.; Buday, T.; Szabó, G. Testing Algorithms for the Identification of Asbestos Roofing Based on Hyperspectral Data. Environ. Eng. Manag. J. 2014, 143, 2875–2880. [Google Scholar] [CrossRef]
- Barrile, V.; Bilotta, G.; Pannuti, F. A Comparison Between Methods—A Specialized Operator, Object Oriented and Pixel-Oriented Image Analysis—To Detect Asbestos Coverages in Building Roofs Using Remotely Sensed Data. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXI ISPRS Congress, Beijing, China, 3–11 July 2008; Volume XXXVII, pp. 427–434. Available online: http://www.isprs.org/proceedings/XXXVII/congress/8_pdf/2_WG-VIII-2/51.pdf (accessed on 22 January 2019).
- Bhaskaran, S.; Paramananda, S.; Ramnarayan, M. Per-Pixel and Object-Oriented Classification Methods for Mapping Urban Features Using Ikonos Satellite Data. Appl. Geogr. 2010, 30, 650–665. [Google Scholar] [CrossRef]
- Taherzadeh, E.; Shafri, H.Z.M.; Mansor, S.; Ashurov, R. A comparison of hyperspectral data and WorldView-2 images to detect impervious surface. In Proceedings of the 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), Shanghai, China, 4–7 June 2012; pp. 1–4. [Google Scholar] [CrossRef]
- Ban, Y.; Jacob, A.; Gamba, P. Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Using a Robust Urban Extractor. ISPRS J. Photogramm. Remote Sens. 2015, 103, 28–37. [Google Scholar] [CrossRef]
- Samsudin, S.H.; Shafri, H.Z.M.; Hamedianfar, A. Development of Spectral Indices for Roofing Material Condition Status Detection Using Field Spectroscopy and Worldview-3 Data. J. Appl. Remote Sens. 2016, 10, 025021–025038. [Google Scholar] [CrossRef]
- Marino, C.M.; Panigada, C.; Busetto, L. Airborne hyperspectral remote sensing applications in urban areas: Asbestos concrete sheeting identification and mapping. In Proceedings of the IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Area, Rome, Italy, 8–9 November 2001; pp. 7541–7544. [Google Scholar]
- Fiumi, L. Evaluation of MIVIS Hyperspectral Data for Mapping Covering Materials. In Proceedings of the IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Rome, Italy, 8–9 November 2001; pp. 324–327. [Google Scholar]
- Bassani, C.; Cavalli, R.M.; Cavalcante, F.; Cuomo, V.; Palombo, A.; Pascucci, S.; Pignatti, S. Deterioration Status of Asbestos-Cement Roofing Sheets Assessed by Analyzing Hyperspectral Data. Remote Sens. Environ. 2007, 109, 361–378. [Google Scholar] [CrossRef]
- Fiumi, L.; Campopiano, A.; Casciardi, S.; Ramires, D. Method Validation for the Identification of Asbestos–Cement Roofing. Appl. Geomat. 2012, 4, 55–64. [Google Scholar] [CrossRef]
- Frassy, F.; Candiani, G.; Maianti, P.; Marchesi, A.; Nodari, F.R.; Rusmini, M.; Albonico, C.; Gianinetto, M. Airborne Remote Sensing for Mapping Asbestos Roofs in Aosta Valley. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2012), Munich, Germany, 22–27 July 2012; pp. 7541–7544. [Google Scholar]
- Frassy, F.; Candiani, G.; Rusmini, M.; Maianti, P.; Marchesi, A.; Rota Nodari, F.; Dalla Via, G.; Albonico, C.; Gianinetto, M. Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps. Sensors 2014, 14, 15900–15913. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Fiumi, L.; Congedo, L.; Meoni, C. Developing Expeditious Methodology for Mapping Asbestos-Cement Roof Coverings over the Territory of Lazio Region. Appl. Geomat. 2014, 6, 37–48. [Google Scholar] [CrossRef]
- Cilia, C.; Panigada, C.; Rossini, M.; Candiani, G.; Pepe, M.; Colombo, R. Mapping of Asbestos Cement Roofs and Their Weathering Status Using Hyperspectral Aerial Images. ISPRS Int. J. Geo-Inf. 2015, 4, 928–941. [Google Scholar] [CrossRef][Green Version]
- Pacifici, F. On the Predictive Value of the WorldView3. VNIR and SWIR Spectral Bands. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016), Beijing, China, 10–15 July 2016; pp. 898–901. [Google Scholar] [CrossRef]
- Taherzadeh, E.; Shafri, H.Z.M. Development of a Generic Model for the Detection of Roof Materials Based on an Object-Based Approach Using Worldview-2 Satellite Imagery. Adv. Remote Sens. 2013, 2, 312–321. [Google Scholar] [CrossRef]
- Gibril, M.B.A.; Shafri, H.Z.M.; Hamedianfar, A. New semi-automated mapping of asbestos cement roofs using rule-based object-based image analysis and Taguchi optimization technique from WorldView-2 images. Int. J. Remote Sens. 2017, 38, 467–491. [Google Scholar] [CrossRef]
- Abriha, D.; Kovács, Z.; Ninsawat, S.; Bertalan, L.; Balázs, B.; Szabó, S. Identification of roofing materials with Discriminant Function Analysis and Random Forest classifiers on pan-sharpened WorldView-2 imagery—A comparison. Hung. Geogr. Bull. 2018, 67, 375–392. [Google Scholar] [CrossRef]
- QGIS. Available online: https://qgis.org/en/site/forusers/download.html (accessed on 15 January 2019).
- Myint, S.W.; Gober, P.; Brazel, A.; Grossman-Clarke, S.; Weng, Q. Per-Pixel vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery. Remote Sens. Environ. 2011, 115, 1145–1161. [Google Scholar] [CrossRef]
- Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 2010, 65, 2–16. [Google Scholar] [CrossRef][Green Version]
- Digital Globe. Available online: https://www.digitalglobe.com/ (accessed on 22 January 2019).
- Jenson, J.R.; Cowen, D.C. Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogramm. Eng. Remote Sens. 1999, 65, 611–622. [Google Scholar]
- Orfeo Tool-Box. Available online: https://www.orfeo-toolbox.org/ (accessed on 22 January 2019).
- Kulkarn, A.D.; Lowe, B. Random Forest Algorithm for Land Cover Classification. Int. J. Recent Innov. Trends Comput. Commun. 2016, 4, 58–63. [Google Scholar]
- Whitcomb, J.; Moghaddam, M.; McDonald, K.; Kellndorfer, J.; Podest, E. Wetlands Map of Alaska Using L-Band Radar Satellite Imagery. Can. J. Remote Sens. 2009, 35, 54–72. [Google Scholar] [CrossRef]
- Tso, B.; Mather, P. Classification Methods for Remotely Sensed Data; Taylor & Francis: London, UK, 2001; pp. 309–326. [Google Scholar]
- Richards, J.A. Classifier performance and map accuracy. Remote Sens. Environ. 1996, 57, 161–166. [Google Scholar] [CrossRef]
- Mclver, D.K.; Friedl, M.A. Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1959–1968. [Google Scholar] [CrossRef]
- SISPC—Tuscany Region. Available online: http://opendata.prevenzionecollettiva.toscana.it/ (accessed on 15 January 2019).
|Description||Panchromatic Image||Multispectral Image|
|Source||Planetek Italia||Planetek Italia|
|Size||40785 × 25825 pixels||10197 × 6458 pixels|
|Number of bands||1||8|
|Radiometric resolution (bit)||16||16|
|Spatial resolution (cm)||50||200|
|Resampling type||Bilinear Interpolation||Bilinear Interpolation|
|Cloud cover (%)||0||0|
|Character set code||utf-8||utf-8|
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Tommasini, M.; Bacciottini, A.; Gherardelli, M. A QGIS Tool for Automatically Identifying Asbestos Roofing. ISPRS Int. J. Geo-Inf. 2019, 8, 131. https://doi.org/10.3390/ijgi8030131
Tommasini M, Bacciottini A, Gherardelli M. A QGIS Tool for Automatically Identifying Asbestos Roofing. ISPRS International Journal of Geo-Information. 2019; 8(3):131. https://doi.org/10.3390/ijgi8030131Chicago/Turabian Style
Tommasini, Maurizio, Alessandro Bacciottini, and Monica Gherardelli. 2019. "A QGIS Tool for Automatically Identifying Asbestos Roofing" ISPRS International Journal of Geo-Information 8, no. 3: 131. https://doi.org/10.3390/ijgi8030131