Metraipolis › archive › dc_73ce38f7cdfd

ASCoR: Asymmetric Semantic-Spatial Learning for Remote Sensing Domain Generalization — arXiv:2611.03382

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03382
License (as archived): CC0-1.0
Archived: 2026-11-03T18:55:27.288Z
SHA-256 of the archived text: 9bfc96ed17937dd1e525c604d5c88fea88f48f43d8d284344ac4e03706f9bd18
Stable URL of this page: https://metraipolis.com/documento/dc_73ce38f7cdfd

How to cite

ASCoR: Asymmetric Semantic-Spatial Learning for Remote Sensing Domain Generalization — arXiv:2611.03382 — arXiv (en), https://arxiv.org/abs/2611.03382 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_73ce38f7cdfd (Metraipolis archive dc_73ce38f7cdfd, sha256 9bfc96ed17937dd1e525c604d5c88fea88f48f43d8d284344ac4e03706f9bd18)

BibTeX

@misc{dc73ce38f7cdfd,
  title = {ASCoR: Asymmetric Semantic-Spatial Learning for Remote Sensing Domain Generalization — arXiv:2611.03382},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03382},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_73ce38f7cdfd; sha256 9bfc96ed17937dd1e525c604d5c88fea88f48f43d8d284344ac4e03706f9bd18},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03382. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Source-only domain generalization (DG) for remote sensing semantic segmentation seeks reliable predictions in unseen domains without target-domain data during training. Although vision foundation models offer transferable knowledge, category recognition remains sensitive to source-specific visual cues, while task-specific adaptation may weaken pretrained transferability. We propose ASCoR, an asymmetric semantic-spatial framework built on SAM3. Its text-conditioned semantic branch uses class-name prompts to provide explicit category guidance. Probe-Once Dynamic Adaptive LoRA distributes a fixed adaptation budget across encoder blocks to limit source-specific specialization. A complementary task-specific dense branch learns multi-scale spatial representations using a separate frozen SAM3 image encoder. A lightweight reliability router integrates the two branches through bounded spatial correction based on local prediction reliability. All components are trained using source-domain supervision only. Experiments on the ISPRS, LoveDA, and OpenEarthMap benchmarks cover nine cross-domain transfers. ASCoR achieves a state-of-the-art mean IoU of 68.61% across six ISPRS transfers, outperforming the previous best method by 2.69 percentage points. It also achieves the best results on all three LoveDA/OpenEarthMap transfers, with 59.07%, 47.95%, and 49.38% mIoU on Rural-to-Urban, Urban-to-Rural, and Urban-to-OpenEarthMap, respectively. Ablation studies, feature visualizations, and spatial reliability analyses support the effectiveness and complementarity of the proposed components. Code: 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/2611.03382 — 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 2026-11-03T18:16:53.070Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).