Add dataset card (README.md)

README.md

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+---
+language:
+- en
+license: cc-by-4.0
+size_categories:
+- 100GB<n<1TB
+task_categories:
+- image-segmentation
+- regression
+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)
+```