Legacy asbestos in built-up urban areas poses a risk of exposure to the surrounding community through environmental exposure pathways. Better understanding the exposure risk of in-situ asbestos containing materials (ACM) presents to surrounding communities through these pathways can better inform its management and reduce asbestos-related diseases. Remote sensing imagery and computer vision techniques facilitate the automated detection of ACM roofs across urban areas. Identifying ACM roofs coupled with GIS-based approaches to evaluate urban community characteristics provides an assessment of community exposure risk to asbestos in a scenario where ACM roofs are disturbed or degraded. This study develops a novel Asbestos-Community Exposure Risk Assessment (A-CERA) framework and associated method and then applies it to a case study urban area located in Australia. A-CERA was developed and applied using a three-phase study method. Phase 1 develops a framework of asbestos exposure risk from literature and subject matter expert focus groups. Phase two formulates the techniques and methods underpinning A-CERA, including autonomous computer vision ACM roof detection, GIS-based land-use feature extraction, and community exposure risk quantification and assessment. Phase 3 applies the developed A-CERA framework to an urban area covering 320 square kilometres with 2,512 detected industrial ACM roofs. Each ACM roof was ranked from the highest assessed level of community exposure risk to the lowest. A-CERA advances the way ACM materials are managed within the urban footprint to reduce asbestos-related diseases.