Given Australia’s significant and aged asbestos legacy, the long-term sustainability of effective and accessible asbestos waste management is a national priority of Australia’s Asbestos National Strategic Plan. The current policy for managing hazardous asbestos waste is via deep burial in landfill. Technological alternatives to approved deep burial landfill methods exist and could be considered innovative and sustainable additional options for managing asbestos waste, where these are proven viable, and where appropriate policy and regulatory changes are implemented. We present a summary of alternative asbestos waste management technologies and discuss issues influencing their potential application in the Australian context. Increasing the options for asbestos waste management in Australia may additionally facilitate the safe, planned removal of asbestos-containing materials (ACMs) from the built environment. Altogether, this will reduce the potential for exposure to asbestos fibres and work towards eliminating asbestos-related disease in Australia, therefore contributing towards achieving the overarching aim of Australia’s Asbestos National Strategic Plan.

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.