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An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Parking spot classification is a fundamental task in intelligent transportation systems, yet most deep learning approaches rely on large amounts of annotated data and exhibit limited generalization across heterogeneous environments. To address these limitations, we investigate a self-taught learning framework based on unsupervised representation learning with convolutional autoencoders. The proposed approach learns transferable visual representations from unlabeled data and reuses the learned encoders as fixed feature extractors for supervised classification with limited annotated samples in t

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