Andreas Sedlmeier
Papers
1
Total Citations
4
H-Index
1
About
Andreas Sedlmeier is a researcher at the forefront of computational geometry and spatial data mining, with a particular focus on the automated analysis of building floor plans. His work addresses the challenge of discovering and learning recurring structural patterns within architectural designs, a key step toward enabling intelligent, data-driven building information modeling (BIM) and urban planning. Sedlmeier's most-cited paper, "Discovering and Learning Recurring Structures in Building Floor Plans" (2017), introduces novel algorithms for identifying repeated spatial configurations—such as room layouts or corridor networks—directly from vectorized floor plan data. This contribution bridges pattern recognition and geometric reasoning, offering a foundation for applications in automated design review, building code compliance, and generative design. While his citation count is still growing, Sedlmeier’s work is notable for its methodological rigor and practical relevance, laying groundwork for future advances in how machines understand and reuse architectural knowledge. His research stands as a valuable resource for students and professionals exploring the intersection of geometry, machine learning, and the built environment.
Research Focus
Key Achievements
Top Papers
- 1Discovering and Learning Recurring Structures in Building Floor Plans4 citations · 2017