{
  "abstract": "Accurate building extraction from remote sensing imagery requires both contextual information and local structural detail. This paper presents LMDE-UNet , an encoder–decoder network that combines window-based local multi-directional scanning (WLMS), a bottleneck feature aggregation module (BFAM), and an edge enhancement module (DEEM) in the skip connections. Each local visual selective scan block models global context before applying local scanning within non-overlapping windows. Experiments on three building-extraction datasets show that LMDE-UNet achieves higher intersection over union (IoU) and F1-scores than the evaluated baselines, with competitive precision and recall. On the Typical Chinese Urban Building Dataset, LMDE-UNet achieves an IoU of 73.47% and an F1-score of 85.16%, exceeding the corresponding SwinUNet scores by 0.99 and 0.63 percentage points, respectively. Its precision reaches 86.73%. The ablation results suggest that WLMS provides larger IoU gains when combined with BFAM and DEEM than when added alone.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Huada Huang"
    },
    {
      "name": "Zhenzhong Huang"
    },
    {
      "name": "Chao Ren"
    },
    {
      "name": "Wei Zhang"
    },
    {
      "name": "Guowei Zhao"
    },
    {
      "name": "Yintao Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0p9n7wxjca066.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-77120-5",
  "fulltext_url": null,
  "id": "0p9n7wxjca066",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-77120-5",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/0p9n7wxjca066.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T18:20:26Z",
      "native_id": "10.1038/s41598-026-77120-5",
      "source": "feed-nature",
      "url": "https://www.nature.com/articles/s41598-026-77120-5"
    },
    {
      "first_seen": "2026-11-05T00:20:35Z",
      "native_id": "10.1038/s41598-026-77120-5",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-77120-5"
    }
  ],
  "subjects": [],
  "title": "LMDE-UNet: local multi-directional state space modeling with dynamic edge enhancement for building extraction from remote sensing images",
  "title_text": "LMDE-UNet: local multi-directional state space modeling with dynamic edge enhancement for building extraction from remote sensing images",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T00:20:51Z",
  "url": "https://research.draconitic.com/paper/0p9n7wxjca066",
  "venue": "Scientific Reports",
  "versions": []
}