Sebastian Feld
Papers
2
Total Citations
8
H-Index
2
About
Sebastian Feld is a researcher whose work centers on indoor navigation, spatial computing, and the computational analysis of architectural environments. His research addresses the growing demand for intelligent navigation systems in complex indoor spaces, particularly focusing on how building topologies and floor plan structures can be leveraged to enhance routing and wayfinding capabilities. In his notable work on scoring alternative routes using implicit building topologies, Feld tackles a practical challenge in modern navigation: providing users with meaningful route choices that go beyond simple shortest-path calculations. His approach enables systems to propose contextually relevant alternatives — such as routes optimized for speed or points of interest — within indoor environments like shopping malls. Complementing this, his research on discovering and learning recurring structures in building floor plans demonstrates an interest in applying machine learning and pattern recognition techniques to architectural data, unlocking new ways to automatically understand and process spatial layouts. While Feld's citation counts remain modest, reflecting either the nascent stage of his career or the specialized nature of his field, his contributions sit at an important intersection of computer science, spatial intelligence, and human-centered navigation — areas with significant growth potential as smart buildings and indoor positioning technologies continue to advance.
Research Focus
Key Achievements
Top Papers
- 1Scoring of alternative routes using implicit building topologies4 citations · 2015
- 2Discovering and Learning Recurring Structures in Building Floor Plans4 citations · 2017