Heba is working on a system to build ubiquitous accessibility digital-maps automatically, where indoor/outdoor spaces are updated with various accessibility semantics and marked with assessments of their accessibility levels for the vision-and mobility-impairment disability types. To build the maps automatically, she presented a passive crowdsourcing approach where the users' smartphone devices' spatiotemporal sensors signals (e.g. barometer, accelerometer, etc.) are analyzed to detect and map the accessibility semantics.
She presented AI-based algorithms to passively detect various semantics such as accessible pedestrian signals and missing curb-ramps. She has also presented a probabilistic framework to construct the map while taking the uncertainty in the detected semantics and the sensors into account. The system was evaluated in two countries and its evaluation results show high detection accuracy for the various accessibility semantics. Moreover, the crowdsourcing framework helps further improve the map integrity overtime. Heba has a PhD in computer science from the University of Maryland and works as a research scientist at Amazon.