Nearly 40 million Americans report a disability, and of this population, 70 percent travel less because of the challenges they face. When they do travel, those with limited mobility are more likely to be pedestrians or public transit users. Today, free commercial routing applications such as Google Maps offer a robust suite of tools for the able-bodied public to walk, ride bikes, take public transportation, or hail a taxi. Yet, such tools for persons with limited mobility to determine a safe and perhaps even pleasant urban route are experimental, limited, and only available in select cities (e.g. accessmap.io, chisafepath.com). This project intervenes by tackling the challenge of missing environmental data. First, I assess a regionally stratified sample of municipalities across the United States on their collection and maintenance of open data on environmental features that impact accessible travel for persons with disabilities. Based on this assessment, I evaluate options for filling in missing curb ramp data using machine learning and supplemental open data such as open street map, LiDAR, and aerial imagery. Finally, I look at the relevance and replicability of these GeoAI methods for filling in missing curb ramp data. Centering the needs of community members with disabilities, this research creates tools for improving mobility, increasing community strength and inclusivity while also critiquing the data driven scientific paradigm.