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.

Artificial Intelligence (AI) is providing the technology for large-scale, cost-effective and current asbestos-containing material (ACM) roofing detection. AI models can provide additional data to monitor, manage and plan for ACM in situ and its safe removal and disposal, compared with traditional approaches alone. Advances are being made in AI algorithms and imagery applied to ACM detection. This study applies mask region-based convolution neural networks (Mask R-CNN) to multi-spectral satellite imagery (MSSI) and high-resolution aerial imagery (HRAI) to detect the presence of ACM roofing on residential buildings across an Australian case study area. The results provide insights into the challenges and benefits of using AI and different imageries for ACM detection, providing future directions for its practical application. The study found model 1, using HRAI and 460 training samples, was the more reliable model of the three with a precision of 94%. These findings confirm the efficacy of combining advanced AI techniques and remote sensing imagery, specifically Mask R-CNN with HRAI, for ACM roofing detection. Such combinations can provide efficient methods for the large-scale detection of ACM roofing, improving the coverage and currency of data for the implementation of coordinated management policies for ACM in the built environment.