Self-Supervised Pretraining for Aerial Road Extraction
This work addresses the costly and scarce high-quality aerial datasets for road extraction, offering a scalable solution for aerial image analysis.
The paper tackles the problem of limited labeled data for aerial image segmentation by proposing a self-supervised pretraining method based on inpainting, which improves segmentation accuracy, particularly in low-data scenarios.
Deep neural networks for aerial image segmentation require large amounts of labeled data, but high-quality aerial datasets with precise annotations are scarce and costly to produce. To address this limitation, we propose a self-supervised pretraining method that improves segmentation performance while reducing reliance on labeled data. Our approach uses inpainting-based pretraining, where the model learns to reconstruct missing regions in aerial images, capturing their inherent structure before being fine-tuned for road extraction. This method improves generalization, enhances robustness to domain shifts, and is invariant to model architecture and dataset choice. Experiments show that our pretraining significantly boosts segmentation accuracy, especially in low-data regimes, making it a scalable solution for aerial image analysis.