The Application of Transformer Model in Building Information Modeling

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The model achieves 95% accuracy in change detection tasks, indicating significant effectiveness in urban monitoring.
Key improvements include a 1.37% increase in mean Intersection over Union (mIoU) and a high Kappa value of 0.795.
The integration of convolutional neural networks with transformers allows for better local feature extraction and global context modeling.
Lightweight design innovations reduce computational costs, making the model suitable for real-time applications.

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This research demonstrates improved change detection in urban settings using a transformer model and satellite imagery.

Zhe Wang (2025) studied this question.

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