This white paper by Mosaic ATM demonstrates how computer vision can automate airport layout detection using satellite imagery. Traditional blueprint updates are labor-intensive and often outdated; AI-driven digitization offers a scalable alternative. Using deep learning models like UNet, the team trained on a dataset of 46 airports to identify runways, taxiways, aprons, blast pads, and buildings. Data augmentation and preprocessing improved accuracy, with the final model achieving 84% validation accuracy. Results show promise for expanding to multiclass segmentation, reducing manual work, and enabling real-time integration with air traffic management tools.

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