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[2502.14894] FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping
Summary
The paper introduces FOCUS, a deep learning framework for mapping PFAS contamination by integrating sparse data with environmental context, enhancing monitoring capabilities.
Why It Matters
PFAS are hazardous environmental contaminants, and traditional monitoring methods are costly and limited. FOCUS leverages AI to improve mapping and understanding of PFAS spread, which is crucial for public health and environmental safety.
Key Takeaways
- FOCUS integrates hydrological and environmental data to enhance PFAS mapping.
- The framework outperforms traditional methods in accuracy and scalability.
- AI can significantly improve environmental monitoring and risk assessment.
Computer Science > Computer Vision and Pattern Recognition arXiv:2502.14894 (cs) [Submitted on 17 Feb 2025 (v1), last revised 17 Feb 2026 (this version, v4)] Title:FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping Authors:Jowaria Khan, Alexa Friedman, Sydney Evans, Rachel Klein, Runzi Wang, Katherine E. Manz, Kaley Beins, David Q. Andrews, Elizabeth Bondi-Kelly View a PDF of the paper titled FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping, by Jowaria Khan and 8 other authors View PDF HTML (experimental) Abstract:Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant public health impacts, yet large-scale monitoring remains severely limited due to the high cost and logistical challenges of field sampling. The lack of samples leads to difficulty simulating their spread with physical models and limited scientific understanding of PFAS transport in surface waters. Yet, rich geospatial and satellite-derived data describing land cover, hydrology, and industrial activity are widely available. We introduce FOCUS, a geospatial deep learning framework for PFAS contamination mapping that integrates sparse PFAS observations with large-scale environmental context, including priors derived from hydrological connectivity, land cover, source proximity, and sampling distance. These priors are integrated into a principled, noise-aware loss, yielding a robust t...
