Jon E. Froehlich
I am a Visiting Faculty Researcher working on the AI for Social Good team to enhance and amplify human abilities through advances in AI + HCI. I am also a Professor in Human-Computer Interaction at UW’s Allen School of Computer Science and Engineering.
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StreetReaderAI: Making Street View Accessible Using Context-Aware Multimodal AI
Alex Fiannaca
Nimer Jaber
Victor Tsaran
Proceedings of the 2025 ACM Symposium on User Interface Software and Technology (UIST'25) (to appear)
Preview abstract
Interactive streetscape mapping tools such as Google Street View (GSV) and Meta Mapillary enable users to virtually navigate and experience real-world environments via immersive 360° imagery but remain fundamentally inaccessible to blind users. We introduce StreetReaderAI, the first-ever accessible street view tool, which combines context-aware, multimodal AI, accessible navigation controls, and conversational speech. With StreetReaderAI, blind users can virtually examine destinations, engage in open-world exploration, or virtually tour any of the over 220 billion images and 100+ countries where GSV is deployed. We iteratively designed StreetReaderAI with a mixed-visual ability team and performed an evaluation with eleven blind users. Our findings demonstrate the value of an accessible street view in supporting POI investigations and remote route planning. We close by enumerating key guidelines for future work.
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“Does the cafe entrance look accessible? Where is the door?” Towards Geospatial AI Agents for Visual Inquiries
Jared Hwang
Zeyu Wang
John S. O'Meara
Xia Su
William Huang
Yang Zhang
Alex Fiannaca
ICCV'25 Workshop "Vision Foundation Models and Generative AI for Accessibility: Challenges and Opportunities" (2025)
Preview abstract
Interactive digital maps have revolutionized how people travel and learn about the world; however, they rely on preexisting structured data in GIS databases (e.g., road networks, POI indices), limiting their ability to address geovisual questions related to what the world looks like. We introduce our vision for Geo-Visual Agents—multimodal AI agents capable of understanding and responding to nuanced visual-spatial inquiries about the world by analyzing large-scale repositories of geospatial images, including streetscapes (e.g., Google Street View), place-based photos (e.g., TripAdvisor, Yelp), and aerial imagery (e.g., satellite photos) combined with traditional GIS data sources. We define our vision, describe sensing and interaction approaches, provide three exemplars, and enumerate key challenges and opportunities for future work.
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