Indoor maps are essential for navigation, resource allocation, and autonomous operation in complex environments, yet creating them at scale has long been impeded by high costs and specialized hardware requirements. We present Tesseract, a modular system that transforms ordinary low-semantic floor plan images into navigable graph structures, without requiring specialized sensors or 3D modeling tools. Through Tesseract, we integrate deep learning modules for text detection and door classification. We then implement a novel floodfill-based segmentation and graph optimization solution. Tesseract ultimately generates semantically rich, compact graph representations of the original floor plans that are computationally parsable for indoor navigation applications. We evaluate Tesseract across two large-scale university buildings as well as a benchmark dataset, demonstrating high navigational completeness despite variations in layout complexity. The system processes floor plans efficiently, with runtime scaling linearly to the number of detected regions, thus remaining practical for large-scale deployments. Graph pruning reduces the initially dense connectivity—typically quadratic in the number of regions—to a sparse structure, yielding up to 78% fewer nodes and 70% fewer edges, all without compromising connectivity. Moreover, geometric fidelity is preserved within 80–86% of true real-world distances. These findings establish Tesseract as a robust and scalable solution, broadening access to automated indoor navigation and spatial analytics.

Brain-Computer Interfaces (BCIs), e.g., Neuralink, are evolving to enable seamless interaction and control across various domains, such as assistive systems and virtual environments. While conventional BCI applications treat users as control units that transmit commands to devices, our vision introduces a paradigm shift toward the novel concept of Human-as-a-Sensor (HaaS), where users themselves function as intelligent multimodal sensing agents. HaaS leverages BCIs to extract contextual spatial information from brain signals as individuals naturally interact with their environment. By decoding neural activity, HaaS complements the limitations of traditional sensors and offers unique advantages for spatially-aware systems. We explore a range of opportunities enabled by HaaS for enhancing spatial awareness and outline multi-disciplinary research challenges in realizing our vision. We also present a feasibility study on using HaaS to track human mobility, showcasing the promise of HaaS for powering future spatially-aware systems.

Recent transportation mode detection systems propose leveraging signals from only the serving cell tower to ensure ubiquity and practical deployability across all phones. However, existing solutions employ limited statistical hand-engineered features and traditional machine learning classifiers, leading to low estimation accuracy.

Designing a globally ubiquitous positioning system that works seamlessly in all environments remains a critical area of ongoing research. While Global Navigation Satellite Systems (GNSS), such as GPS, are the predominant technology for global outdoor positioning, they fail in areas with physical obstructions (e.g., dense urban regions and indoors) and are prone to jamming. These limitations significantly restrict their global accessibility and dependability, highlighting the need for supplementary positioning technologies.

Floor identification has gained much attention due to the increasing demand for indoor location-based services, especially prompt emergency response services. Leveraging Cellular signals for floor identification has been of recent interest due to the pervasiveness of cellular technology. However, all current systems rely on information from multiple cell towers concurrently, which is inaccessible in most phones and thus severely limits their deployability.