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An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data — arXiv:2609.03258

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.03258
License (as archived): CC0-1.0
Archived: 2026-10-17T02:56:12.691Z
SHA-256 of the archived text: 9a554a47dafb7526fe9b5dce4f43388f0917bb3e2bc69319604cb68326b8da1e
Stable URL of this page: https://metraipolis.com/documento/dc_ff46963b414e

How to cite

An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data — arXiv:2609.03258 — arXiv (en), https://arxiv.org/abs/2609.03258 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff46963b414e (Metraipolis archive dc_ff46963b414e, sha256 9a554a47dafb7526fe9b5dce4f43388f0917bb3e2bc69319604cb68326b8da1e)

BibTeX

@misc{dcff46963b414e,
  title = {An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data — arXiv:2609.03258},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.03258},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff46963b414e; sha256 9a554a47dafb7526fe9b5dce4f43388f0917bb3e2bc69319604cb68326b8da1e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.03258. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 the target domain. To further enhance robustness and mitigate architectural bias, an ensemble of heterogeneous autoencoders is employed, with independent classifier heads and prediction fusion at inference time. Experiments conducted on the PKLot and CNRPark benchmarks under cross-dataset evaluation protocols show that the proposed ensemble-based strategy substantially reduces annotation requirements while improving robustness under significant domain shifts, achieving accuracies between 93\% and 96\% in data-constrained scenarios.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-04 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.03258 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T02:27:18.944Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-04?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).