Abstract
Mining of sand, gravel, and stone, collectively referred to as aggregates, has increased in recent years due to growing usage of these materials in developing urban landscapes and increasing living standards, especially in African countries that are pursuing rapid economic growth. The rise in demand, however, has wide-ranging consequences for the environment from which the material is extracted. Nevertheless, our knowledge of where the material is extracted from remains insufficient, which hinders assessments of the overall sustainability of extraction. Here, we provide a nationwide distribution of sand extraction in Rwanda by applying deep learning on highly refined resolution aerial imagery. We show that mining sites increased by 18% to 3,751 between 2008 and 2023, which are spatially linked to river valleys and urban development at the sub-watershed level. We further reveal that the expansion of sand extraction over the past decade often encroached into agricultural areas. Our approach offers a scalable way to monitor sand extraction activities to assess their implications on the natural and built environment.
| Original language | English |
|---|---|
| Article number | 101236 |
| Journal | One Earth |
| Volume | 8 |
| Issue number | 3 |
| ISSN | 2590-3330 |
| DOIs | |
| Publication status | Published - 2025 |
Bibliographical note
Publisher Copyright:© 2025
Keywords
- deep learning
- highly refined resolution satellite imagery
- mining
- natural resources
- resource extraction
- sand extraction
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