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Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery

Author

Listed:
  • Xian Sun

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Dongshuo Yin

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Fei Qin

    (University of Chinese Academy of Sciences)

  • Hongfeng Yu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Wanxuan Lu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Fanglong Yao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Qibin He

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Xingliang Huang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Zhiyuan Yan

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Peijin Wang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Chubo Deng

    (Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Nayu Liu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Yiran Yang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Wei Liang

    (Chinese Academy of Sciences)

  • Ruiping Wang

    (Chinese Academy of Sciences)

  • Cheng Wang

    (Xiamen University
    Fujian Collaborative Innovation Center for Big Data Applications in Governments)

  • Naoto Yokoya

    (RIKEN Center for Advanced Intelligence Project, RIKEN
    The University of Tokyo)

  • Ronny Hänsch

    (German Aerospace Center (DLR))

  • Kun Fu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

Abstract

With the advancement of global civilisation, monitoring and managing dumpsites have become essential parts of environmental governance in various countries. Dumpsite locations are difficult to obtain in a timely manner by local government agencies and environmental groups. The World Bank shows that governments need to spend massive labour and economic costs to collect illegal dumpsites to implement management. Here we show that applying novel deep convolutional networks to high-resolution satellite images can provide an effective, efficient, and low-cost method to detect dumpsites. In sampled areas of 28 cities around the world, our model detects nearly 1000 dumpsites that appeared around 2021. This approach reduces the investigation time by more than 96.8% compared with the manual method. With this novel and powerful methodology, it is now capable of analysing the relationship between dumpsites and various social attributes on a global scale, temporally and spatially.

Suggested Citation

  • Xian Sun & Dongshuo Yin & Fei Qin & Hongfeng Yu & Wanxuan Lu & Fanglong Yao & Qibin He & Xingliang Huang & Zhiyuan Yan & Peijin Wang & Chubo Deng & Nayu Liu & Yiran Yang & Wei Liang & Ruiping Wang & C, 2023. "Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37136-1
    DOI: 10.1038/s41467-023-37136-1
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