Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review

Abstract

Building roofing produced with asbestos-containing materials is a significant concern due to its detrimental health hazard implications. Efficiently locating asbestos roofing is essential to proactively mitigate and manage potential health risks from this legacy building material. Several studies utilised remote sensing imagery and machine learning-based image classification methods for mapping roofs with asbestos-containing materials. However, there has not yet been a critical review of classification methods conducted in order to provide coherent guidance on the use of different remote sensing images and classification processes. This paper critically reviews the latest works on mapping asbestos roofs to identify the challenges and discuss possible solutions for improving the mapping process. A peer review of studies addressing asbestos roof mapping published from 2012 to 2022 was conducted to synthesise and evaluate the input imagery types and classification methods. Then, the significant challenges in the mapping process were identified, and possible solutions were suggested to address the identified challenges. The results showed that hyperspectral imagery classification with traditional pixel-based classifiers caused large omission errors. Classifying very-high-resolution multispectral imagery by adopting object-based methods improved the accuracy results of ACM roof identification; however, non-optimal segmentation parameters, inadequate training data in supervised methods, and analyst subjectivity in rule-based classifications were reported as significant challenges. While only one study investigated convolutional neural networks for asbestos roof mapping, other applications of remote sensing demonstrated promising results using deep-learning-based models. This paper suggests further studies on utilising Mask R-CNN segmentation and 3D-CNN classification in the conventional approaches and developing end-to-end deep semantic classification models to map roofs with asbestos-containing materials.

WaRM: A Roof Material Spectral Library for Wallonia, Belgium

“…Spectral libraries provide spectral properties of materials and components in a wide range of wavelengths. In urban areas, the exploitation of surface and building spectral properties is of increasing importance for a broad range of applications: urban climatechange modeling [1]; assessment of the heating/cooling effects of urban materials in urban heat-island mitigation strategies [2,3]; or identification of surface materials such as roof materials, for mapping and inventories [4], for weathering-condition status detection [5], detection of specific/dangerous materials such as asbestos [6,7], and detection of solar panels [8] or metallic roofs [9].…”

Explainable Automatic Detection of Fiber–Cement Roofs in Aerial RGB Images

“…This amounts to an area of 823.4375 km 2 , which, in turn, corresponds to 527 aerial photographs, each with dimensions of 5000 × 5000 pixels and a spatial resolution of 20 cm (resampled from 25 cm) in RGB composition, acquired from the Institut Cartogràfic i Geològic de Catalunya (Catalan Cartographic and Geologic Institute; ICGC hereafter) (https: //www.icgc.cat/, accessed on 1 February 2024). Each photograph represents 1.5625 km 2 .…”

“…On the other hand, the automatic detection of asbestos-containing rooftops has been carried out in different ways, and often with remarkable success (see Section 2). In all cases, high-resolution, multi-band remote sensing images were used in combination with machine learning algorithms [2]. In this situation, if the quality and resolution of imagery is satisfactory, the accuracy of asbestos rooftop identification should be assured by using modern technology.…”

Does the Presence of Asbestos-Containing Materials in Buildings Post-construction and Before Demolition Have an Impact on the Exposure to Occupants in Non-occupational Settings?

“…The length of five microns may also be challenged as it may have an arbitrary existence in the first place [15]. Thirdly, corrugated sheet roofing has already been gaining attention [23][24][25]. Studies on asbestos sheet roofing, prevalent in Asia, may be performed to further the understanding on the effect by measurement in cases of non-occupational risk.…”