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.
