{
  "abstract": "To address the misclassification of visually similar ground objects and inaccurate boundary localization caused by background interference, as well as small-object omission, fragmented regions, and incomplete boundaries resulting from scale variations, this study proposes MASwin-Unet for semantic segmentation of complex mining areas. The model incorporates a Spatially Adaptive Feature Modulation Module (SAFM) to suppress background noise and similar-texture interference while enhancing key regions and boundary details. It also introduces a Multi-Scale Context Fusion Module (MSCF) to integrate contextual information across different receptive fields and improve multi-scale feature modeling. An experimental dataset was constructed from UAV imagery of the Dongshan Xiuqing Mining Area, and experiments were also conducted on AeroScapes and LoveDA. MASwin-Unet achieved an OA of 97.18%, a Kappa coefficient of 95.79%, an mIoU of 85.14%, and an mF1 of 90.73%, outperforming BEDSN, FADC, DeepLabV3 + , PSPNet, HRNet, SegFormer, TransUNet, U-Net, and Swin-Unet. The results demonstrate good generalization on public datasets and show that the model effectively reduces similar-object misclassification, small-object omission, and incomplete boundary segmentation in complex UAV scenes.",
  "abstract_shown": true,
  "affiliations": [
    "School of Emergency Technology and Management, University of Emergency Management"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Qing Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Mingxing Lu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yan Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dwappcwwmr43q.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-69579-z",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-69579-z",
  "id": "dwappcwwmr43q",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-69579-z",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2214|Remote-Sensing Image Classification"
    ]
  ],
  "openalex": "W7211914447",
  "pdf_url": "https://www.nature.com/articles/s41598-026-69579-z_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/dwappcwwmr43q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:56Z",
      "native_id": "1fecf122121b479ea05fb822760586e5",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-69579-z"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "UAV image segmentation in complex mining areas by integrating spatially adaptive feature modulation and multi-scale context fusion modules",
  "title_text": "UAV image segmentation in complex mining areas by integrating spatially adaptive feature modulation and multi-scale context fusion modules",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:56Z",
  "url": "https://research.draconitic.com/paper/dwappcwwmr43q",
  "venue": "Scientific Reports",
  "versions": []
}