A study published in Nature Communications describes an efficient and intelligent dumpsite detection technique and analyses the correlation between the number of dumpsites and social/economic factors.
This work is proposed by a research team of the Key Laboratory of Network Information System Technology at the Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS).
The team constructed the first fine-grained remote-sensing dumpsite dataset with optical satellite images (Figure 1) and built a highly automated method for dumpsite detection combined with deep learning methods. This approach reduces the investigation time by more than 96.8% compared with the manual method.
The study focuses on the global distribution of dumpsites in urban areas (Figure 2). By analyzing the statistical correlation between the number of dumpsites and 18 social/economic factors in 28 cities worldwide, the study shows that the number of dumpsites is statistically correlated with factors such as development, urbanization and sanitation level but not with population density and education level.
This work improves the efficiency of dumpsite detection and provides a powerful detection tool. With this novel methodology, it is now capable of analyzing the relationship between dumpsites and various social attributes on a global scale, temporally and spatially.
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