When our company, SISL India, received a challenging project from the Middle East — delivering 3 million building footprints within an extremely short timeframe — we knew we had to push the boundaries of conventional geospatial data processing. In this article, I’ll walk you through our technical journey and explain how our SOTA single-stage detection framework not only met but exceeded the performance of many commercially and openly available building extraction models.
The Challenge
Traditional two-stage approaches rely on region proposals followed by iterative refinements. While these methods can be effective, they are computationally intensive and require lengthy training times. With aggressive project deadlines, we prioritized a streamlined, detection-based solution that balances speed and accuracy. This approach allowed us to extract and validate building footprints even at a fine scale of 1:500, ensuring both cleanliness and high accuracy.
Our Technical Approach
1. A Robust Architecture Built for Speed and Precision
Backbone: CSPDarknet53: Our model leverages a CSPDarknet53-based backbone — a network architecture designed with cross-stage partial connections. This design enhances gradient flow, reduces computational overhead, and captures the multi-scale features necessary for precise building footprint detection.
Detection Head: Fully Convolutional and Integrated: We employ a fully convolutional detection head that predicts both bounding boxes and segmentation masks in a single forward pass. By unifying detection and segmentation tasks, we eliminate the multiple passes typical of two-stage methods, significantly cutting down on inference time.
Model Complexity: Balancing Detail and Efficiency: Our implementation comprises approximately 86 million trainable parameters. This balanced complexity enables the model to capture intricate structural details while maintaining rapid performance.
2. Training on a Massive Dataset
To ensure robustness and generalization, we trained our model on an extensive dataset:
Annotated Samples: Approximately 400K building footprints.
Our training regime incorporated advanced data augmentation and multi-scale training techniques. We also developed customized loss functions to balance localization, classification, and segmentation tasks — ensuring that every aspect of the model’s performance was finely tuned.
3. Performance Metrics and Real-Time Inference
Segmentation Performance
Precision: 94%
Recall: 90%
Mean Average Precision (mAP@50 mask): 0.56667
Mean Average Precision (mAP@50–95 mask): 0.45386
These metrics are not just numbers — they represent the accuracy and reliability of our building footprint extraction, which is critical for urban planning, cadastral mapping, and infrastructure development.
Inference Speed and Time Efficiency: Thanks to our optimized single-stage detection framework powered by CSPDarknet53, our approach achieves significantly faster inference times compared to traditional two-stage models. Conventional methods would require 2–3 times longer training due to the overhead of region proposal refinements and segmentation. Our solution allows us to extract high-quality building footprints in a fraction of the time, making it ideal for large-scale urban mapping and rapid geospatial analysis.
4. Outperforming Commercially and Openly Available Models
One of the standout achievements of our approach is its ability to exceed the performance benchmarks set by many commercial and open-source building extraction models. By optimizing both the architecture and the training methodology, our model demonstrates superior scalability and efficiency — delivering high-precision results at a scale that many existing solutions struggle to match. This breakthrough is particularly significant in contexts where rapid, high-quality geospatial data processing is essential.
Conclusion
Our experience on this Middle Eastern project reinforces a key insight: with the right optimizations, rapid and high-accuracy geospatial data processing is entirely achievable. By leveraging a SOTA single-stage detection framework built on CSPDarknet53 and an integrated detection head, we managed to deliver 3 million building footprints at a remarkable speed without compromising quality.
With approximately 86 million trainable parameters (22% fewer than the previous gen standard), extensive training on a large-scale dataset, and impressive performance metrics — 94% segmentation precision and 90% recall — our model not only meets but surpasses the capabilities of many commercial and open-source alternatives. This achievement underscores our commitment to innovation and operational excellence, paving the way for future advancements in urban analytics and infrastructure development.
Final Thoughts and Lessons Learned
Throughout this project, our team learned that innovation often emerges at the intersection of necessity and creativity. The challenges we encountered pushed us to rethink traditional methods and adopt a more integrated, efficient approach. As urban environments continue to grow and evolve, the ability to rapidly process geospatial data will become increasingly vital. We’re excited to continue refining our techniques and exploring new applications for our technology, ultimately transforming how cities are planned and built.
To know more — Contact Us at yash@saitech-sytems.com
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