Enterprise AI Analysis
FloodVision: Urban Flood Depth Estimation Using Foundation Vision-Language Models and Domain Knowledge Graph
FloodVision leverages GPT-4o and a structured knowledge graph to provide accurate, zero-shot urban flood depth estimation, significantly outperforming prior methods and enhancing real-time disaster response.
Executive Impact Summary
FloodVision introduces a groundbreaking zero-shot framework for urban flood depth estimation, addressing critical limitations of existing computer vision methods. By integrating the semantic reasoning of GPT-4o with a physically grounded knowledge graph, FloodVision achieves a mean absolute error (MAE) of 8.17 cm, representing a 20.5% reduction compared to a GPT-4o-only baseline. This advancement enables generalizable, near real-time depth estimations across diverse urban scenes, crucial for emergency response and smart city resilience. Its ability to mitigate quantitative hallucination through physical grounding makes it reliable for safety-critical applications like road accessibility mapping and infrastructure damage assessment.
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MAE Reduction vs. Baseline
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Mean Absolute Error
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Improved Pearson r Correlation
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Problem Statement
Solution Overview
Methodology
Key Findings
Core Components
Real-world Impact
Future Directions
The Challenge of Accurate Flood Depth Estimation
Traditional flood depth estimation methods, from field surveys to advanced computer vision techniques, face significant limitations in urban environments. These include slow data acquisition, sparse spatial coverage, high computational costs, and poor generalization across diverse scenes. Existing computer vision models often rely on fixed object detectors and task-specific training, limiting their scalability and adaptability. Crucially, Large Vision-Language Models (VLMs), while powerful, often suffer from 'quantitative hallucination'—generating plausible but incorrect numerical estimates due to a lack of physical grounding, making them unreliable for precise measurements in safety-critical applications.
FloodVision: A Grounded VLM Approach
FloodVision addresses these challenges by presenting a novel zero-shot framework that integrates the semantic scene interpretation capabilities of a foundation Vision-Language Model (VLM) like GPT-4o with a structured, physically grounded domain knowledge graph (FloodKG). This approach enables accurate and generalizable flood depth estimation without task-specific training. By dynamically identifying visible reference objects, querying canonical dimensions from FloodKG, estimating submergence ratios, and applying statistical filtering, FloodVision mitigates VLM hallucinations and provides reliable depth measurements.
Enterprise Process Flow
Input RGB Image
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GPT-4o: Reference Object Identification
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Query FloodKG for Verified Object Dimensions
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GPT-4o: Estimate Submergence Ratios
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Post-processing: Submerged Ratio × Object Height
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Statistical Outlier Filtering
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Aggregate Results (Min/Avg/Max Depth)
8.17cm
Mean Absolute Error (MAE)
FloodVision achieves a mean absolute error of just 8.17 cm on crowdsourced images, demonstrating high precision for urban flood depth estimation.
| Method | MAE (cm) | Pearson r | Key Differentiators |
|---|---|---|---|
| GPT-4o-only (baseline) | 10.28 | 0.44 | Lacks physical grounding, prone to quantitative hallucination. |
| FloodVision (Max) | 9.40 | 0.45 | FloodVision variant, less robust than average. |
| FloodVision (Min) | 8.44 | 0.50 | FloodVision variant, good outlier handling. |
| FloodVision (Average) | 8.17 | 0.51 | Proposed method: Combines GPT-4o with FloodKG for physical grounding and zero-shot generalization. |
| Prior CNN-based (Chaudhary et al. [4], Li et al. [7]) | ~10 | N/A | Requires specific, visible reference objects; limited generalization, task-specific training. |
| Prior GPT-4 based (Akinboyewa et al. [1]) | 25.00 | N/A | GPT-4 only, lacks domain knowledge for hallucination mitigation. |
Core Components of FloodVision
- Foundation Vision-Language Model (VLM): Leverages OpenAI GPT-4o's strong image-text alignment and zero-shot reasoning for identifying diverse reference objects (e.g., car tires, curbs) in complex visual scenes and estimating their submergence ratios.
- Urban Flood Scene Knowledge Graph (FloodKG): A structured repository of canonical real-world dimensions for common urban objects (vehicles, people, infrastructure). It grounds the VLM's reasoning in physical reality, mitigating quantitative hallucinations by providing verified height values and enabling accurate depth calculation.
- Prompt Engineering: A unified three-step strategy guides GPT-4o through object identification, measurement estimation, and structured JSON output, ensuring machine-readability and robustness.
- Post-processing: Includes canonicalization of identified objects against FloodKG, multiplication of submergence ratios by object heights, statistical outlier filtering (e.g., fully submerged objects), and aggregation into minimum, average, and maximum depth estimates.
Real-world Impact: MyCoast New York Deployment
Evaluated on 110 crowdsourced images from the MyCoast New York platform, FloodVision demonstrates its practicality for real-world scenarios. It provides timely and accurate depth estimates, essential for real-time road accessibility mapping, flood damage assessment, and informing emergency response. Its near real-time operation makes it suitable for integration into digital twin platforms and citizen-reporting apps, significantly enhancing smart city flood resilience.
Future Directions and Enhancements
While FloodVision marks a significant advance, future work includes incorporating additional visual cues like water surface texture and reflections to improve depth inference beyond object-based reasoning. Exploring few-shot or reinforcement learning can further enhance adaptability. Generating synthetic urban flood images will expand generalization capabilities. Integrating temporal change checks for video streams and spatiotemporal modeling will enable real-time flood monitoring and dynamic progression analysis across urban road networks, moving towards more comprehensive smart city flood resilience systems.
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