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A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation — arXiv:2610.21238

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.21238
License (as archived): CC0-1.0
Archived: 2026-10-16T10:35:12.944Z
SHA-256 of the archived text: f6b5a6f4534a25eec4a2bd1967119a36ea99c185dc0fe7fc6d19ac01eb14d027
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How to cite

A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation — arXiv:2610.21238 — arXiv (en), https://arxiv.org/abs/2610.21238 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_abfee0fee47d (Metraipolis archive dc_abfee0fee47d, sha256 f6b5a6f4534a25eec4a2bd1967119a36ea99c185dc0fe7fc6d19ac01eb14d027)

BibTeX

@misc{dcabfee0fee47d,
  title = {A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation — arXiv:2610.21238},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.21238},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_abfee0fee47d; sha256 f6b5a6f4534a25eec4a2bd1967119a36ea99c185dc0fe7fc6d19ac01eb14d027},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.21238. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A few well-annotated datasets for land-cover semantic segmentation have recently been introduced to advance the field of earth observation technologies. However, these datasets overlook the significant diversity among geographic areas with different climates, which can greatly impact and diversify land cover. Consequently, this leads to a domain gap in remote sensing images and severe performance degradation of the segmentation models. To enhance land-cover semantic segmentation with improved generalization ability, we conducted the first investigation into the impact of climate on this task. In this paper, we present a unique large-scale Climate-Aware Satellite Images Dataset (CASID) specifically designed for domain adaptive land-cover semantic segmentation. It consists of 980 satellite images with a size of 5000 x 5000 pixels, collected from 30 different regions around Asia, covering over 24,500 square kilometers. These images are gathered from four distinct climate zones, namely temperate monsoon, subtropical monsoon, tropical monsoon, and tropical rainforest. It includes four sub-datasets/domains, each representing one of the aforementioned climate zones. This characteristic makes CASID the first climate-aware land-cover semantic segmentation dataset with multiple domains. Additionally, we provide a comprehensive analysis of the samples from the four climate zones, emphasizing differences in global image features, image texture, category distribution, spectral value, and object shape. These analyses offer valuable insights for subsequent research in this field. Moreover, we conduct extensive experiments to evaluate the latest semantic segmentation and unsupervised domain adaptation methods on the CASID dataset. These results serve as a robust baseline for future research endeavors. Our dataset will be made publicly available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.21238 — 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 2026-10-16T08:52:02.643Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).