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Multi-Resolution UAV Remote Sensing for Anthropogenic Debris Detection in Complex River Environments



Details

  • Journal Title:
    Remote Sensing
  • Personal Author:
  • NOAA Program & Office:
    NOS (National Ocean Service)
  • Description:
    Anthropogenic debris in urban floodplains poses significant environmental and ecological risks, with an estimated 4 to 12 million metric tons entering oceans annually via riverine transport. While remote sensing and artificial intelligence (AI) offer promising tools for automated debris detection, most existing datasets focus on marine environments with homogeneous backgrounds, leaving a critical gap for complex terrestrial floodplains. This study introduces the San Diego River Debris Dataset, a multi-resolution UAV imagery collection with ground reference designed to support automated detection of anthropogenic debris in urban floodplains. The dataset includes manually annotated debris objects captured under diverse environmental conditions using two UAV platforms (DJI Matrice 300 and DJI Mini 2) across spatial resolutions ranging from 0.4 to 4.4 cm. We benchmarked five deep learning architectures (RetinaNet, SSD, Faster R-CNN, DetReg, Cascade R-CNN) to assess detection accuracy across varying image resolutions and environmental settings. Cascade R-CNN achieved the highest accuracy (0.93) at 0.4 cm resolution, with accuracy declining rapidly at resolutions above 1 cm and 3.3 cm. Spatial analysis revealed that 51% of debris was concentrated within unsheltered encampments, which occupied only 2.6% of the study area. Validation confirmed a strong correlation between predicted debris extent and field measurements, supporting the dataset’s operational reliability. This openly available dataset fills a gap in environmental monitoring resources and provides guides for future research and deployment of UAV-based debris detection systems in urban floodplain areas.
  • Source:
    Remote Sensing, 17(13), 2172
  • DOI:
  • ISSN:
    2072-4292
  • Format:
    pdf
  • Publisher:
    MDPI AG|https://ror.org/00ckv2g77
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA21NOS9990109
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Submitted
  • Main Document Checksum:
    urn:sha-512:4c9eda6d7667ae5d032445873fe64151c2e9f6bebf4e90dc6bc03394ba5c969feae3450381821cd6c7fae31a72aed82817bc8342d0c6766ec79ec88253b5ea42
  • Download URL:
  • File Type:
    Filetype[PDF - 5.80 MB ]
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