A QGIS Tool for Automatically Identifying Asbestos Roofing
1
PIN—Polo Universitario Città di Prato, Piazza Giovanni Ciardi 25, 59100 Prato, Italy
2
Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Florence, Italy
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(3), 131; https://doi.org/10.3390/ijgi8030131
Received: 25 January 2019 / Revised: 10 February 2019 / Accepted: 24 February 2019 / Published: 6 March 2019
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
Exposure to asbestos fibers implies a long-term risk for human health; therefore, the development of information systems that are able to detect the extent and status of asbestos over a certain territory has become a priority. This work presents a tool (based on the geographic information system open source software, QGIS) that is conceived for automatically identifying buildings with asbestos roofing. The area under investigation is the metropolitan area around Prato (Italy). The performance analysis of this system was carried out by classifying images that were acquired by the WorldView-3 sensor. These images are available at a low cost when compared with those obtained by means of aerial surveys, and they provide adequate resolution levels for roofing classification. The tool, a QGIS plugin, has shown fairly good performance in identifying asbestos roofing, with some false negatives and some false positives when applying a per-pixel classification. A performance improvement is obtainable when considering the percentage of asbestos pixels that are contained in each roof of the analyzed image. This value is also available with the plugin. In the future, this tool should make it possible to monitor the asbestos roof removal process over time in the area of interest, in accordance with other image data that give evidence of such removals.
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Keywords:
asbestos identification; image analysis; open source geographic information system; remote sensing
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MDPI and ACS Style
Tommasini, M.; Bacciottini, A.; Gherardelli, M. A QGIS Tool for Automatically Identifying Asbestos Roofing. ISPRS Int. J. Geo-Inf. 2019, 8, 131.
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