Enhanced River Planform Analysis Using Deep Learning and Medial Axis Transform with Sentinel‑1A Imagery
APA Cite: Thapa, P., Davis, L., Amanambu, A., LaFevor, M., & Frame, J. (2025). Enhanced river planform analysis using deep learning and medial axis transform with Sentinel 1A imagery. Earth Surface Processes and Landforms, 50(12), e70158.
🌊 Introduction: Why Mapping Rivers Matters
Rivers are constantly changing — they migrate, widen, narrow, and shift across their floodplains. Tracking these changes is essential for:
- Flood hazard planning
- River restoration
- Dam impact analysis
- Ecological management
- Understanding long‑term geomorphic evolution
However, accurately detecting river centerlines and water surface widths has been a persistent challenge, especially using satellite data.
Clouds, vegetation, watercolor, turbidity, and seasonal variability make optical imagery inconsistent.
This is where SAR (Synthetic Aperture Radar) and deep learning provide a major advantage.
🤖 A New Hybrid Method
Our study introduces a new approach that integrates:
1️⃣ DeepLabV3 – A State‑of‑the‑Art Deep Learning Model
DeepLabV3 is a powerful semantic segmentation model that can extract water bodies from Sentinel‑1A SAR imagery even when:
- Clouds block optical sensors
- The terrain is complex
- Traditional thresholding fails
The model provides a clean and accurate water mask, which is the foundation for all subsequent measurements.
2️⃣ Medial Axis Transform (MAT) – Precision Geometry
Once the river boundaries are extracted, the Medial Axis Transform:
- Finds the geometric centerline
- Computes the distance to the riverbanks
- Estimates water surface width
- Preserves complex curvature information
This produces a continuous river centerline and precise width measurements — essential for analyzing river planform dynamics.
📌 Study Areas
We tested this approach in multiple river systems across the Southeastern United States, including:
- Sipsey River (unregulated)
- Coosa River (regulated)
- Tennessee River (large, engineered system)
These sites represent a wide range of morphological and hydrological conditions.
📈 Key Findings
- DeepLabV3 performed consistently in diverse flows, seasons, and river types.
- MAT generated smooth, accurate centerlines, even for highly sinuous reaches.
- The workflow successfully estimated river widths at high spatial consistency.
- SAR‑based analysis captured changes invisible in optical imagery.
- The pipeline is fully reproducible and scalable for large river systems.
🧩 Why This Matters
This hybrid approach improves river monitoring by offering:
✔ Reliable measurements under clouds and floods
✔ Automated, repeatable analysis across large regions
✔ Better quantification of geomorphic change
✔ New tools for hydrologists, geomorphologists, and remote sensing scientists
The method can support:
- Flood early‑warning systems
- Dam impact assessment
- River migration prediction models
- Machine learning–based river classifications
- Climate‑driven water resource studies
🔬 Conclusion
This research shows that combining deep learning with geometric transforms is a powerful way to map rivers more accurately than ever before.
Using Sentinel‑1A SAR ensures robust monitoring even in challenging conditions.
Our method provides:
- High‑precision centerlines
- Reliable width estimates
- Strong performance across diverse river systems
This approach opens new doors for large‑scale river analysis and future AI‑driven hydrological research.
Links: Earth Surface Processes and Landforms | Geomorphology Journal | Wiley Online Library
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