Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference
arXiv:2605.24106v1 Announce Type: new Abstract: Rapid and accurate flood extent mapping from Remote Sensing data, such as Synthetic Aperture Radar (SAR), is critical for operational disaster response, but standard Deep Learning models often produce physically impossible predictions due to a lack of hydrological constraints. While PhysicsInformed Neural Networks (PINNs) attempt to address this by embedding governing laws directly into the loss function, their application to real-world remote sensing data frequently fails. Enforcing rigid spatial derivatives (e.g., the 2D Shallow Water Equations
The increasing availability of remote sensing data and the critical need for rapid disaster response, coupled with the inherent limitations of deep learning models in respecting physical laws, necessitate robust solutions like PINNs.
Improving the accuracy and reliability of flood mapping through Physics-Informed Neural Networks (PINNs) has direct implications for disaster response, resource allocation, and climate change adaptation, especially in regions vulnerable to extreme weather events.
The development of a heteroscedastic uncertainty framework for PINN-based flood inference addresses a key failure point in applying PINNs to real-world remote sensing data, making these advanced AI models more practical for critical environmental monitoring.
- · Disaster relief organizations
- · Insurance industry
- · Hydrological modeling researchers
- · Earth observation data providers
- · Traditional flood modeling firms (if slow to adapt)
- · Regions without advanced remote sensing capabilities
More accurate and timely flood predictions enable faster and more effective humanitarian aid and resource deployment.
Improved flood risk assessment could lead to more precise insurance policies and infrastructure development in vulnerable areas.
The success of physics-informed AI in earth observation may accelerate its adoption across other critical environmental and climate modeling domains.
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Read at arXiv cs.LG