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Related Experiment Video

Updated: Sep 22, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Research on Building Space Model Method Based on Big Data Map Visual Design.

Qiang Xu¹, Long He²

¹School of Architecture, Tianjin University, Tianjin 300072, China.

Computational Intelligence and Neuroscience

|May 23, 2022

Summary

This summary is machine-generated.

A new big data map visualization method enhances architectural design by using a multidimensional aggregation pyramid model. This approach significantly speeds up Building Information Modeling (BIM) data loading and visualization compared to traditional methods.

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Area of Science:

Computer Science
Architecture
Data Visualization

Background:

Existing methods for visualizing architectural space models face challenges with large datasets and complex information.
The tile pyramid model offers 2D spatial aggregation but lacks multidimensional capabilities for time, space, and attributes.

Purpose of the Study:

To propose and validate a novel big data map visualization method for architectural space models.
To develop a multidimensional aggregation pyramid model (MAP) supporting hierarchical aggregation of time, space, and attributes.
To create an efficient visualization framework for Building Information Modeling (BIM) data.

Main Methods:

Developed a multidimensional aggregation pyramid model (MAP) extending the tile pyramid concept.
Utilized Apache Spark for parallel preprocessing and HBase for data storage, creating the MAP-Vis framework.
Reconstructed BIM models, split IFC files using a hierarchical strategy, and transformed them into glTF format.
Implemented a visibility detection algorithm using hierarchical bounding volumes (BVH) for scene components.

Main Results:

The proposed MAP-Vis framework demonstrates significantly faster loading times for glTF formatted BIM data (approx. 7s) compared to IFC (approx. 40s).
The glTF format proved more suitable for BIM model data visualization than IFC.
The big data map visualization design, display, and interaction based on glTF format were effective.

Conclusions:

The developed big data map visualization method, utilizing the MAP model and glTF format, is effective for architectural space models.
The MAP-Vis framework offers a scalable and efficient solution for visualizing complex BIM data.
The study confirms the superiority of glTF over IFC for efficient BIM data visualization.