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Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery — arXiv:2609.40212

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.40212
License (as archived): CC0-1.0
Archived: 2026-10-07T14:46:37.465Z
SHA-256 of the archived text: d53dda96f5e3bf4a49b40c7571b6086ec7895cf1649dcf181923912b4979d4dc
Stable URL of this page: https://metraipolis.com/documento/dc_34fda460e87f

How to cite

Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery — arXiv:2609.40212 — arXiv (en), https://arxiv.org/abs/2609.40212 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_34fda460e87f (Metraipolis archive dc_34fda460e87f, sha256 d53dda96f5e3bf4a49b40c7571b6086ec7895cf1649dcf181923912b4979d4dc)

BibTeX

@misc{dc34fda460e87f,
  title = {Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery — arXiv:2609.40212},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.40212},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_34fda460e87f; sha256 d53dda96f5e3bf4a49b40c7571b6086ec7895cf1649dcf181923912b4979d4dc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.40212. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery. The tested solutions in-cluded numerous vegetation indices (VIs) thresholding and neural networks (NNs). The analysis was conducted for two study areas for which surveys were carried out using different UAVs and cameras. The ground sampling distances for the study areas were 10 mm and 15 mm, respectively. Reference classification was performed manually, obtaining approximately 24 million classified pix-els for the first area and approximately 3.8 million for the second. This research study included an analysis of the impact of the season on the threshold values for the tested VIs and the impact of image patch size provided as inputs for the NNs on classification accuracy. The results of the con-ducted research study indicate a higher classification accuracy using NNs (about 96%) compared with the best of the tested VIs, i.e., Excess Blue (about 87%). Due to the highly imbalanced nature of the used datasets (non-urbanized areas constitute approximately 87% of the total datasets), the Mat-thews correlation coefficient was also used to assess the correctness of the classification. The analysis based on statistical measures was supplemented with a qualitative assessment of the classification results, which allowed the identification of the most important sources of differences in classification between VIs thresholding and NNs.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-10-01 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.40212 — 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 2026-10-07T14:19:00.850Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).