Engineering and Technology

Revealing Influencing Factors on Global Waste Distribution via Deep-learning Based Dumpsite Detection From Satellite Imagery

X. Sun, D. Yin, et al.

This innovative research conducted by Xian Sun and colleagues introduces a deep convolutional network capable of identifying dumpsites in high-resolution satellite images, significantly reducing investigation time. The model analyzed data from 28 cities globally, revealing important correlations between dumpsites and urban development metrics.

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Abstract

This paper presents a deep convolutional network for detecting dumpsites in high-resolution satellite imagery. The model, applied to 28 cities globally, detected nearly 1000 dumpsites, reducing investigation time by over 96.8% compared to manual methods. This approach enables large-scale analysis of the relationship between dumpsites and social attributes, revealing correlations with development, urbanization, and sanitation but not with population, education, or technology.

Publisher

Nature Communications

Published On

Mar 15, 2023

Authors

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, Cheng Wang, Naoto Yokoya, Ronny Hänsch, Kun Fu

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