Geovisualizations are powerful tools for communicating spatial information, but are inaccessible to screen-reader users. To address this limitation, we present GeoVisA11y, an LLM-based question-answering system that makes geovisualizations accessible through natural language interaction. The system supports map reading, analysis, interpretation and navigation by handling analytical, geospatial, visual, and contextual queries. Through user studies with six screen-reader users and six sighted participants, we demonstrate that GeoVisA11y effectively bridges accessibility gaps while revealing distinct interaction patterns between user groups. We contribute: (1) an open-source, accessible geovisualization system, (2) empirical findings on query and navigation differences, and (3) a dataset of geospatial queries to inform future research on accessible data visualization.

The output quality of large language models (LLMs) can be improved via “reasoning”: generating segments of chain-of-thought (CoT) content to further condition the model prior to producing user-facing output. While these chains contain valuable information, they are verbose and lack explicit organization, making them tedious to review. Moreover, they lack opportunities for user feedback, such as removing unwanted considerations, adding desired ones, or clarifying unclear assumptions. We introduce Interactive Reasoning, an interaction design that visualizes chain-of-thought outputs as a hierarchy of topics and enables user review and modification. We implement interactive reasoning in Hippo, a prototype for AI-assisted decision making in the face of uncertain trade-offs. In a user study with 16 participants, we find that interactive reasoning in Hippo allows users to quickly identify and interrupt erroneous generations, efficiently steer the model towards customized responses, and better understand both model reasoning and model outputs. Our work contributes to a new paradigm that incorporates user oversight into LLM reasoning processes.

Accessible parking is critical for people with disabilities (PwDs), allowing equitable access to destinations, independent mobility, and community participation. Despite mandates, there has been no large-scale investigation of the quality or allocation of disability parking in the US nor significant research on PwD perspectives and uses of disability parking. In this paper, we first present a semi-structured interview study with 11 PwDs to advance understanding of disability parking uses, concerns, and relevant technology tools. We find that PwDs often adapt to disability parking challenges according to their personal mobility needs and value reliable, real-time accessibility information. Informed by these findings, we then introduce a new deep learning pipeline, called AccessParkCV, and parking dataset for automatically detecting disability parking and inferring quality characteristics (e.g., width) from orthorectified aerial imagery. We achieve a micro-F1=0.89 and demonstrate how our pipeline can support new urban analytics and end-user tools. Together, we contribute new qualitative understandings of disability parking, a novel detection pipeline and open dataset, and design guidelines for future tools.

Figure 1: We introduce GeoQA 3 , a novel accessible AI-based question-answering system for geovisualizations designed for screen-reader users.(A) Through a custom query pipeline, we combine geo-statistical analysis with an LLM to balance accuracy and performance.(B) Users can navigate the map through natural language commands or keyboard controls and (C) zoom in to view county-level data.The AI Chat system is context-aware, taking into account user interactions.See video for demonstration.

Despite diverse mobility needs worldwide, existing mapping tools fail to address the varied experiences of different mobility device users. This paper presents a large-scale online survey exploring how five mobility groups -- users of canes, walkers, mobility scooters, manual wheelchairs, and motorized wheelchairs -- perceive sidewalk barriers. Using 52 sidewalk barrier images, respondents evaluated their confidence in navigating each scenario. Our findings (N=190) reveal variations in barrier perceptions across groups, while also identifying shared concerns. To further demonstrate the value of this data, we showcase its use in two custom prototypes: a visual analytics tool and a personalized routing tool. Our survey findings and open dataset advance work in accessibility-focused maps, routing algorithms, and urban planning.

We have entered a new era of computing—one where AI permeates every aspect of society from education to healthcare. In this workshop, we examine the emerging role of AI in the design of equitable and accessible cities, transportation systems, and interactive tools for mapping and navigation. We will solicit short papers around key Urban AI + disability themes, including autonomous vehicles, intelligent wheelchairs, assistive human-robotic interaction, assessing and navigating pedestrian pathways, indoor accessibility, and overarching challenges related to ethics, bias, and data privacy and security. We invite both traditional HCI and accessibility researchers as well as scholars and practitioners from other disciplines relevant to this workshop, including disability studies, gerontology, social work, community psychology, and law. Our overarching goal is to identify open challenges, share current work across disciplines, and spur new collaborations related to AI and urban accessibility.

We examine the feasibility of using deep learning to infer 33 classes of sidewalk accessibility conditions in pre-cropped streetscape images, including bumpy, brick/cobblestone, cracks, height difference (uplifts), narrow, uneven/slanted, pole, and sign. We present two experiments: first, a comparison between two state-of-the-art computer vision models, Meta’s DINOv2 and OpenAI’s CLIP-ViT, on a cleaned dataset of ∼ 24k images; second, an examination of a larger but noisier crowdsourced dataset (∼ 87k images) on the best performing model from Experiment 1. Though preliminary, Experiment 1 shows that certain sidewalk conditions can be identified with high precision and recall, such as missing tactile warnings on curb ramps and grass grown on sidewalks, while Experiment 2 demonstrates that larger but noisier training data can have a detrimental effect on performance. We contribute an open dataset and classification benchmarks to advance this important area.

Despite decades of effort, pedestrian infrastructure in cities continues to be unsafe or inaccessible to people with disabilities. In this paper, we examine the potential of community-driven digital civics to assess sidewalk accessibility through a deployment study of an open-source crowdsourcing tool called Project Sidewalk. We explore Project Sidewalk’s potential as a platform for civic learning and service. Specifically, we assess its effectiveness as a tool for community members to learn about human mobility, urban planning, and accessibility advocacy. Our findings demonstrate that community-driven digital civics can support accessibility advocacy and education, raise community awareness, and drive pro-social behavioral change. We also outline key considerations for deploying digital civic tools in future community-led accessibility initiatives.

Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge. Traditional quality control measures, such as prescreening workers and refining instructions, often focus solely on optimizing economic output. This paper explores just-in-time AI interventions to enhance both labeling quality and domain-specific knowledge among crowdworkers. We introduce LabelAId, an advanced inference model combining Programmatic Weak Supervision (PWS) with FT-Transformers to infer label correctness based on user behavior and domain knowledge. Our technical evaluation shows that our LabelAId pipeline consistently outperforms state-of-the-art ML baselines, improving mistake inference accuracy by 36.7% with 50 downstream samples. We then implemented LabelAId into Project Sidewalk, an open-source crowdsourcing platform for urban accessibility. A between-subjects study with 34 participants demonstrates that LabelAId significantly enhances label precision without compromising efficiency while also increasing labeler confidence. We discuss LabelAId’s success factors, limitations, and its generalizability to other crowdsourced science domains.

Geovisualizations are powerful tools for exploratory spatial analysis, enabling sighted users to discern patterns, trends, and relationships within geographic data. However, these visual tools have remained largely inaccessible to screen-reader users. We introduce AltGeoViz, a new interactive geovisualization approach that dynamically generates alt-text descriptions based on the user’s current map view, providing voiceover summaries of spatial patterns and descriptive statistics. In a remote user study with five screen-reader users, we found that participants were able to interact with spatial data in previously infeasible ways, demonstrated a clear understanding of data summaries and their location context, and could synthesize spatial understandings of their explorations. Moreover, we identified key areas for improvement, such as the addition of spatial navigation controls and comparative analysis features.

Public transportation provides vital connectivity to people with disabilities, facilitating access to work, education, and health services. While modern navigation applications provide a suite of information about transit options—including real-time updates about bus or train arrivals—they lack data about the accessibility of the transit stops themselves. Bus stop features such as seatings, shelters, and landing areas are critical, but few cities provide this information. In this demo paper, we introduce BusStopCV, a Human+AI web prototype for scalably collecting data on bus stop features using real-time computer vision and human labeling. We describe BusStopCV’s design, custom training with the YOLOv8 model, and an evaluation of 100 randomly selected bus stops in Seattle, WA. Our findings demonstrate the potential of BusStopCV and highlight opportunities for future work.

Online synchronous tutoring allows for immediate engagement between instructors and audiences over distance. However, tutoring physical skills remains challenging because current telepresence approaches may not allow for adequate spatial awareness, viewpoint control of the demonstration activities scattered across an entire work area, and the instructor’s sufficient awareness of the audience. We present Asteroids, a novel approach for tangible robotic telepresence, to enable workbench-scale physical embodiments of remote people and tangible interactions by the instructor. With Asteroids, the audience can actively control a swarm of mini-telepresence robots, change camera positions, and switch to other robots’ viewpoints. Demonstrators can perceive the audiences’ physical presence while using tangible manipulations to control the audience’s viewpoints and presentation flow. We conducted an exploratory evaluation for Asteroids with 12 remote participants in a model-making tutorial scenario with an architectural expert demonstrator. Results suggest our unique features benefitted participants’ engagement, sense of presence, and understanding.