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As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through applications that integrate diverse sensors with real-time decision-making. Streetscape applications—focusing on challenges like pedestrian safety and adaptive traffic management—depend on managing distributed, heterogeneous sensor data, aligning information across time and space, and enabling real-time processing. These tasks are inherently complex and often difficult to scale. The Streetscape Application Services Stack (SASS) addresses these challenges with three core services: multimodal data synchronization, spatiotemporal data fusion, and distributed edge computing. By structuring these capabilities as clear, composable abstractions with clear semantics, SASS allows developers to scale streetscape applications efficiently while minimizing the complexity of multimodal integration. We evaluated SASS in two real-world testbed environments: a controlled parking lot and an urban intersection in a major U.S. city. These testbeds allowed us to test SASS under diverse conditions, demonstrating its practical applicability. The Multimodal Data Synchronization service reduced temporal misalignment errors by 88%, achieving synchronization accuracy within 50 milliseconds. Spatiotemporal Data Fusion service improved detection accuracy for pedestrians and vehicles by over 10%, leveraging multicamera integration. The Distributed Edge Computing service increased system throughput by more than an order of magnitude. Together, these results show how SASS provides the abstractions and performance needed to support real-time, scalable urban applications, bridging the gap between sensing infrastructure and actionable streetscape intelligence. 

Blind and low-vision (BLV) people rely on GPS-based systems for outdoor navigation. GPS's inaccuracy, however, causes them to veer off track, run into obstacles, and struggle to reach precise destinations. While prior work has made precise navigation possible indoors via hardware installations, enabling this outdoors remains a challenge. Interestingly, many outdoor environments are already instrumented with hardware such as street cameras. In this work, we explore the idea of repurposing existing street cameras for outdoor navigation. Our community-driven approach considers both technical and sociotechnical concerns through engagements with various stakeholders: BLV users, residents, business owners, and Community Board leadership. The resulting system, StreetNav, processes a camera's video feed using computer vision and gives BLV pedestrians real-time navigation assistance. Our evaluations show that StreetNav guides users more precisely than GPS, but its technical performance is sensitive to environmental occlusions and distance from the camera. We discuss future implications for deploying such systems at scale 

The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scale poses significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for modeling circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of a massive number of local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe.