{
  "abstract": "Flood disasters pose persistent challenges to urban safety and emergency management. Traditional remote sensing technologies remain limited in real-time capability and spatial coverage. In surveillance-based urban flood monitoring, direct physical measurement of flood depth is often unavailable; therefore, flood risk level detection based on visual reference objects provides a feasible alternative for rapid flood assessment. However, existing lightweight YOLO models still struggle to accurately identify submerged targets under partial submergence, large-scale variation, and complex urban conditions. To address these issues, this study proposes UFD-YOLO, a lightweight framework for urban flood risk level detection using pedestrians and vehicles as multi-scale visual references. First, a reference-based flood risk level criterion was established according to the submergence states of pedestrians and vehicles, and a dataset covering diverse urban scenarios was constructed. Second, the DySnakeConv, AIFI, and DetectAux were progressively integrated into YOLO11n to improve local structural perception, strengthen feature interaction, and enhance multi-scale supervisory learning. Compared with YOLO11n, UFD-YOLO improves Precision, Recall, mAP50, and mAP50:95 by 2.5%, 5.1%, 5.8%, and 2.6%, respectively, while maintaining low computational cost. Additional evaluations further demonstrate its accurate and stable performance in challenging scenarios. Overall, this study provides an effective technical approach for urban flood risk assessment.",
  "abstract_shown": true,
  "affiliations": [
    "China Meteorological Administration Hydro-Meteorology Key Laboratory, College of Hydrology and Water Resources, Hohai University",
    "School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology",
    "Key Laboratory of Urban Meteorology, China Meteorological Administration",
    "The Business School, Beijing Normal University",
    "Pearl River Water Resources Research Institute",
    "School of Computer Engineering, Nanjing Institute of Technology",
    "National Institute of Education, Nanyang Technological University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiaquan Wan"
    },
    {
      "affs": [
        1
      ],
      "name": "Yannian Cheng"
    },
    {
      "affs": [
        2
      ],
      "name": "Junchao Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Ranyu Liu"
    },
    {
      "affs": [
        3
      ],
      "name": "Hao Song"
    },
    {
      "affs": [
        4
      ],
      "name": "Wei Zhang"
    },
    {
      "affs": [
        5
      ],
      "name": "Xing Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Fengchang Xue"
    },
    {
      "affs": [
        6
      ],
      "name": "Jingyu Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Quan J. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Tao Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2fw27bsb5z5rm.bib",
  "cited_by": 0,
  "doi": "10.1038/s44304-026-00224-5",
  "fulltext_url": "https://doi.org/10.1038/s44304-026-00224-5",
  "id": "2fw27bsb5z5rm",
  "issns": [
    "2948-2100"
  ],
  "landing_url": "https://doi.org/10.1038/s44304-026-00224-5",
  "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",
      "QC851-999"
    ],
    [
      "lcc",
      "TA495"
    ],
    [
      "openalex",
      "2306|Flood Risk Assessment and Management"
    ]
  ],
  "openalex": "W7166686066",
  "pdf_url": "https://www.nature.com/articles/s44304-026-00224-5_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/2fw27bsb5z5rm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:35Z",
      "native_id": "94ea0e1e447d4b9f8c6466da5a4bd41f",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s44304-026-00224-5"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2306|Flood Risk Assessment and Management",
      "subfield": "climate"
    }
  ],
  "title": "UFD-YOLO: a lightweight multi-reference model for urban flood risk level detection from surveillance images",
  "title_text": "UFD-YOLO: a lightweight multi-reference model for urban flood risk level detection from surveillance images",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:35Z",
  "url": "https://research.draconitic.com/paper/2fw27bsb5z5rm",
  "venue": "npj Natural Hazards",
  "versions": []
}