| --- |
| language: |
| - en |
| license: cc-by-4.0 |
| size_categories: |
| - 100GB<n<1TB |
| task_categories: |
| - image-segmentation |
| - time-series-forecasting |
| tags: |
| - climate |
| - flood |
| - geospatial |
| - remote-sensing |
| - physics-ai |
| - time-series |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| --- |
| |
| # FloodSimBench: A High-Resolution Physical Flood Inundation Benchmark for AI |
|
|
| 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). |
|
|
| ## 1. Overview |
| - **Objective:** Provide a high-fidelity dataset for training and evaluating AI surrogate models for flood risk assessment and real-time forecasting. |
| - **Domain:** Diverse urban topographies across 10 major US cities (coastal, inland, flat, and hilly). |
| - **Core Value:** Standardized inputs (DEM, Rainfall) and outputs (Water Depth) at 1m resolution to enable comparable research in the AI community. |
|
|
| ## 2. Dataset Specifications |
| - **Spatial Resolution:** 1 meter. |
| - **Domain Size:** Approximately 4km x 4km per city tile. |
| - **File Format:** GeoTIFF (.tif). |
| - **Simulated Event:** |
| - **Forcing:** 1 hour of rainfall (00H-01H) at varying frequencies (10yr, 25yr, 50yr, 100yr), followed by 5 hours of recession (no rain). |
| - **Total Duration:** 6 hours. |
| - **Temporal Resolution:** 5-minute intervals (72 frames per scenario). |
|
|
| ## 3. Metadata & Scenarios |
| Rainfall intensities based on return periods for the covered cities: |
|
|
| | City ID | Description | 100-yr (mm) | 50-yr (mm) | 25-yr (mm) | 10-yr (mm) | |
| |---------|-------------|------------|------------|------------|------------| |
| | HOU | Houston | 129 | 110 | 98 | 82 | |
| | AUS | Austin | 121 | 106 | 93 | 76 | |
| | DAL | Dallas | 94 | 85 | 76 | 65 | |
| | OKC | Oklahoma City | 101 | 89 | 78 | 64 | |
| | LA | Los Angeles | 41 | 36 | 31 | 25 | |
| | SF | San Francisco | 33 | 29 | 26 | 22 | |
| | NYC | New York City | 73 | 65 | 58 | 48 | |
| | ATL | Atlanta | 83 | 74 | 65 | 54 | |
| | ORL | Orlando | 100 | 93 | 85 | 74 | |
| | MIA | Miami | 133 | 118 | 104 | 86 | |
|
|
| ## 4. Benchmark Tasks |
| ### Task A: Static Risk Assessment (Segmentation) |
| **Goal:** Predict the maximum inundation extent and severity over the 6-hour event. |
| - **Input:** Static DEM, Slope, and Total Rainfall Amount. |
| - **Output:** 5-class Semantic Segmentation Map: |
| 0. No Flood (0 - 0.1m) |
| 1. Nuisance (0.1 - 0.2m) |
| 2. Minor (0.2 - 0.3m) |
| 3. Moderate (0.3 - 0.5m) |
| 4. Major (> 0.5m) |
|
|
| ### Task B: Spatiotemporal Forecasting (Recursive) |
| **Goal:** Real-time recursive flood forecasting. |
| - **Input:** Static DEM and Dynamic Flood Depth sequence ($t-11 \dots t$, previous 1 hour). |
| - **Forcing:** Rainfall intensity at $t$ and future steps. |
| - **Output:** Predicted flood depth at future time steps (e.g., $t+1 \dots t+12$). |
|
|
| ## 5. Dataset Structure |
| ``` |
| . |
| ├── 6hr_max/ # Maximum depth maps for Task A |
| │ └── {CityID}_{Rain}mm_MaxDepth.tif |
| ├── {CityID}{TileID}/ # Full time-series for Task B |
| │ ├── {CityID}_DEM.tif # Static Digital Elevation Model |
| │ └── {CityID}_{Rain}mm_WaterDepth_{Time}.tif # Temporal depth frames |
| └── cities_rainfall.json # Metadata for rainfall intensities |
| ``` |
|
|
| ## 6. Data Splitting Strategy |
| To ensure robustness and test generalization to unseen topographies, we split by City: |
| - **Train:** HOU, DAL, OKC, NYC, ATL, ORL |
| - **Validation:** AUS, MIA |
| - **Test:** LA, SF |
|
|
| ## 7. Citation |
| If you use this dataset in your research, please cite: |
| ``` |
| FloodSimBench: A Benchmark Dataset for Training Foundational Flood Inundation Models. ESS Open Archive. 05 May 2026. |
| DOI: https://doi.org/10.22541/essoar.15002741/v1 |
| ``` |
|
|
