Remote sensing for asbestos roofing mapping: Mantua case study using WorldView-3 images. in Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy / Spectrochim Acta A Mol Biomol Spectrosc. 2026 Mar 5;348(Pt 1):127160. doi: 10.1016/j.saa.2025.127160. Epub 2025 Nov 7. (2026)

Tipo pubblicazione

Journal Article; Review

Autori/Collaboratori (6)
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  • Aurigemma ADICMA, Department of Chemical Engineering, Materials and Environment, Sapienza - University of Rome, Via Eudossiana 18, 00184 Rome, Italy. Electronic address: alice.aurigemma@uniroma1.it.
  • Serranti SDICMA, Department of Chemical Engineering, Materials and Environment, Sapienza - University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
  • Paglietti FDepartment of new Technologies for Occupational Safety of Industrial Plants, Products and Human Settlements, INAIL - Italian Workers' Compensation Authority - Research Division, Via Roberto Ferruzzi, 38/40, 00143 Roma, Italy.
  • et alii...

Abstract

Asbestos remains a significant problem in many countries worldwide because of its advantageous physical and mechanical properties that led to widespread use during the 20th century. Nowadays, health risks of asbestos exposure are well known. Both the European and the Italian legislations banned the manufacturing, importation, processing and distribution in commerce of asbestos-containing products and recommended action plans for the mapping and safe removal of asbestos from public and private buildings. On an international scale, this precautionary approach is expanding overseas, providing for its extensive and systematic removal. One of the most common and recurring risk situations worldwide is the presence of asbestos in cement roofing. Aim of this work is to present the application of remote sensing to asbestos cement (AC) roof mapping in Mantua (Italy) municipality. This study has been realized using images collected in August 2023 by WorldView-3 satellite, which provides multispectral images with high spatial resolution. The processing was carried out using Envi version 6.0 and ArcGIS Pro software. Maximum Likelihood (ML) and Spectral Angle Mapper (SAM) were the two main algorithms used and compared in this study. The ML classifier showed the best accuracy (87 %) in identifying AC roofs, while the SAM only obtained 62 % accuracy for the AC roofing in Mantua.

PMID: 41237733