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

Dumpsites are hard to locate globally. Here the authors apply deep networks to satellite images to provide an effective and low-cost way to detect dumpsites with the new method saving more than 96.8% of the manual time with a strong sensitivity to dumpsites. 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.

Automated summary

This study proposes an effective and low-cost method for detecting dumpsites using deep convolutional networks applied to satellite images. Compared with manual methods, the new method saves more than 96.8% of the investigation time while maintaining strong sensitivity to dumpsites. The approach allows for the timely and cost-efficient detection of dumpsites, which is crucial for environmental governance in various countries.