Sand Mining Watch: Leveraging Earth Observation Foundation Models to Inform Sustainable Development (Proposals Track)

Ando Shah (UC Berkeley); Suraj R Nair (UC Berkeley); Tom Boehnel (TU Munich); Joshua Blumenstock (University of California, Berkeley)

Paper PDF Poster File Recorded Talk NeurIPS 2023 Poster Cite
Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning


As the major ingredient of concrete and asphalt, sand is vital to economic growth, and will play a key role in aiding the transition to a low carbon society. However, excessive and unregulated sand mining in the Global South has high socio-economic and environmental costs, and amplifies the effects of climate change. Sand mines are characterized by informality and high temporal variability, and data on the location and extent of these mines tends to be sparse. We propose to build custom sand-mine detection tools by fine-tuning foundation models for earth observation, which leverage self supervised learning - a cost-effective and powerful approach in sparse data regimes. Our preliminary results show that these methods outperform fully supervised approaches, with the best performing model achieving an average precision score of 0.57 for this challenging task. These tools allow for real-time monitoring of sand mining activity and can enable more effective policy and regulation, to inform sustainable development.

Recorded Talk (direct link)