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--- |
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language: |
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- en |
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license: cc-by-4.0 |
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size_categories: |
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- 100GB<n<1TB |
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task_categories: |
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- image-segmentation |
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- time-series-forecasting |
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tags: |
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- climate |
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- flood |
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- geospatial |
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- remote-sensing |
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- physics-ai |
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- time-series |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: mask |
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dtype: image |
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--- |
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# FloodSimBench: A High-Resolution Physical Flood Inundation Benchmark for AI |
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FloodSimBench is a standardized, high-resolution (1m) benchmark dataset designed for urban flood inundation modeling. It bridges the gap between complex physical hydraulic modeling and deep learning foundation models (e.g., Transformers, CNNs). |
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## 1. Overview |
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- **Objective:** Provide a high-fidelity dataset for training and evaluating AI surrogate models for flood risk assessment and real-time forecasting. |
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- **Domain:** Diverse urban topographies across 10 major US cities (coastal, inland, flat, and hilly). |
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- **Core Value:** Standardized inputs (DEM, Rainfall) and outputs (Water Depth) at 1m resolution to enable comparable research in the AI community. |
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## 2. Dataset Specifications |
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- **Spatial Resolution:** 1 meter. |
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- **Domain Size:** Approximately 4km x 4km per city tile. |
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- **File Format:** GeoTIFF (.tif). |
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- **Simulated Event:** |
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- **Forcing:** 1 hour of rainfall (00H-01H) at varying frequencies (10yr, 25yr, 50yr, 100yr), followed by 5 hours of recession (no rain). |
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- **Total Duration:** 6 hours. |
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- **Temporal Resolution:** 5-minute intervals (72 frames per scenario). |
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## 3. Metadata & Scenarios |
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Rainfall intensities based on return periods for the covered cities: |
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| City ID | Description | 100-yr (mm) | 50-yr (mm) | 25-yr (mm) | 10-yr (mm) | |
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|---------|-------------|------------|------------|------------|------------| |
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| HOU | Houston | 129 | 110 | 98 | 82 | |
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| AUS | Austin | 121 | 106 | 93 | 76 | |
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| DAL | Dallas | 94 | 85 | 76 | 65 | |
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| OKC | Oklahoma City | 101 | 89 | 78 | 64 | |
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| LA | Los Angeles | 41 | 36 | 31 | 25 | |
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| SF | San Francisco | 33 | 29 | 26 | 22 | |
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| NYC | New York City | 73 | 65 | 58 | 48 | |
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| ATL | Atlanta | 83 | 74 | 65 | 54 | |
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| ORL | Orlando | 100 | 93 | 85 | 74 | |
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| MIA | Miami | 133 | 118 | 104 | 86 | |
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## 4. Benchmark Tasks |
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### Task A: Static Risk Assessment (Segmentation) |
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**Goal:** Predict the maximum inundation extent and severity over the 6-hour event. |
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- **Input:** Static DEM, Slope, and Total Rainfall Amount. |
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- **Output:** 5-class Semantic Segmentation Map: |
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0. No Flood (0 - 0.1m) |
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1. Nuisance (0.1 - 0.2m) |
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2. Minor (0.2 - 0.3m) |
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3. Moderate (0.3 - 0.5m) |
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4. Major (> 0.5m) |
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### Task B: Spatiotemporal Forecasting (Recursive) |
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**Goal:** Real-time recursive flood forecasting. |
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- **Input:** Static DEM and Dynamic Flood Depth sequence ($t-11 \dots t$, previous 1 hour). |
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- **Forcing:** Rainfall intensity at $t$ and future steps. |
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- **Output:** Predicted flood depth at future time steps (e.g., $t+1 \dots t+12$). |
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## 5. Dataset Structure |
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``` |
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. |
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├── 6hr_max/ # Maximum depth maps for Task A |
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│ └── {CityID}_{Rain}mm_MaxDepth.tif |
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├── {CityID}{TileID}/ # Full time-series for Task B |
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│ ├── {CityID}_DEM.tif # Static Digital Elevation Model |
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│ └── {CityID}_{Rain}mm_WaterDepth_{Time}.tif # Temporal depth frames |
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└── cities_rainfall.json # Metadata for rainfall intensities |
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``` |
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## 6. Data Splitting Strategy |
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To ensure robustness and test generalization to unseen topographies, we split by City: |
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- **Train:** HOU, DAL, OKC, NYC, ATL, ORL |
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- **Validation:** AUS, MIA |
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- **Test:** LA, SF |
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## 7. Citation |
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If you use this dataset in your research, please cite: |
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``` |
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(Citation info pending publication) |
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``` |
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