| id |
caadria2024_514 |
| authors |
Yildiz, Burak, Cuartero, Javier, Mostafavi, Fatemeh and Khademi, Seyran |
| year |
2024 |
| title |
BatchPlan: A Large Scale Solution for Floor Plan Extraction |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.201
|
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 201–210 |
| summary |
The development of Building Information Modelling (BIM) has enabled new opportunities, such as standard data storage and collaborative building design. Moreover, there exist many Life Cycle Assessment (LCA) tools and Building Energy Performance (BEP) simulators that use the Industry Foundation Classes (IFC) exports of BIM platforms as input for further operational analysis. While the extracted IFC files contain numerical and tabular data from the BIM model, the visual data including floor plans and section drawings is often obtained directly from the original 3D software such as REVIT. In this study, we introduce an open-source solution, BatchPlan, for batch processing IFC files of medium- and high-rise building projects, leading to floor plan extraction on a large scale. Furthermore, we have designed a user-friendly graphical interface that allows users to select floors manually. BatchPlan is based on open-source Python packages; thus users can easily edit and adapt it to their specific requirements. The presented solution enables a scalable data generation pipeline for downstream tasks that require extensive quantitative analysis, such as machine learning models to perform material detection, volume estimation, and environmental impact prediction. |
| keywords |
floor plan extraction, Industry Foundation Classes (IFC), Building Information Modelling (BIM), architectural technical drawings, big data |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,253,434 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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