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---
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:
```
(Citation info pending publication)
```
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