---
license: mit
---
# Flood Detection Dataset

## Quick Start
```python
# Example: Loading and filtering data for Dolo Ado in SE Ethiopia, one of the sites explored in our paper (4.17°N, 42.05°E)
import pandas as pd
import rasterio

# Load parquet data
df = pd.read_parquet('N03/N03E042/N03E042-post-processing.parquet')

# Apply recommended filters
filtered_df = df[
    (df.dem_metric_2 < 10) &
    (df.soil_moisture_sca > 1) &
    (df.soil_moisture_zscore > 1) &
    (df.soil_moisture > 20) &
    (df.temp > 0) &
    (df.land_cover != 60) &
    (df.edge_false_positives == 0)
]

# Load corresponding geotiff
with rasterio.open('N03/N03E042/N03E042-90m-buffer.tif') as src:
    flood_data = src.read(1)  # Read first band
```

## Overview
This dataset provides flood detection data from satellite observations. Each geographic area is divided into 3° × 3° tiles (approximately 330km × 330km at the equator).

### What's in each tile?
1. **Parquet file** (post-processing.parquet): Contains detailed observations with timestamps, locations, and environmental metrics
2. **90-meter buffer geotiff** (90m-buffer.tif): Filtered flood extent with 90m safety buffer
3. **240-meter buffer geotiff** (240m-buffer.tif): Filtered flood extent with wider 240m safety buffer

Note: for some tiles, there are no flood detections after the filtering is applied. In these cases, you will see a parquet file but no geotiffs.

## Finding Your Area of Interest
1. Identify the coordinates of your area
2. Round down to the nearest 3 degrees for both latitude and longitude
3. Use these as the filename. For example:
   - For Dolo Ado (4.17°N, 42.05°E)
   - Round down to (3°N, 42°E)
   - Look for file `N03E042` in the `N03` folder

## Directory Structure
```
├── N03                                        # Main directory by latitude
│   ├── N03E042                                # Subdirectory for specific tile
│   │   ├── N03E042-post-processing.parquet    # Tabular data
│   │   ├── N03E042-90m-buffer.parquet         # Geotiff with 90m buffer
│   │   └── N03E042-240m-buffer.tif            # Geotiff with 240m buffer
```

## Data Description

### Parquet File Schema
| Column | Type | Description | Example Value |
|--------|------|-------------|---------------|
| year | int | Year of observation | 2023 |
| month | int | Month of observation | 7 |
| day | int | Day of observation | 15 |
| lat | float | Latitude of detection | 27.842 |
| lon | float | Longitude of detection | 30.156 |
| filename | str | Sentinel-1 source file | 'S1A_IW_GRDH_1SDV...' |
| land_cover | int | ESA WorldCover class | 40  |
| dem_metric_1 | float | Pixel slope | 2.5 |
| dem_metric_2 | float | Max slope within 240m | 5.8 |
| soil_moisture | float | LPRM soil moisture % | 35.7 |
| soil_moisture_zscore | float | Moisture anomaly | 2.3 |
| soil_moisture_sca | float | SCA soil moisture % | 38.2 |
| soil_moisture_sca_zscore | float | SCA moisture anomaly | 2.1 |
| temp | float | Avg daily min temp °C | 22.4 |
| edge_false_positives | int | Edge effect flag (0=no, 1=yes) | 0 |

### Land Cover Classes
Common values in the `land_cover` column:
- 10: Tree cover
- 20: Shrubland
- 30: Grassland
- 40: Cropland
- 50: Urban/built-up
- 60: Bare ground (typically excluded)
- 70: Snow/Ice
- 80: Permanent Water bodies (excluded in this dataset)
- 90: Wetland

## Recommended Filtering
To reduce false positives, apply these filters:

```python
recommended_filters = {
    'dem_metric_2': '< 10',     # Exclude steep terrain
    'soil_moisture_sca': '> 1',  # Ensure meaningful soil moisture
    'soil_moisture_zscore': '> 1', # Above normal moisture
    'soil_moisture': '> 20',    # Sufficient moisture present
    'temp': '> 0',             # Above freezing
    'land_cover': '!= 60',     # Exclude bare ground
    'edge_false_positives': '= 0' # Remove edge artifacts
}
```

## Spatial Resolution
Current data resolution (as of Oct 31, 2024):
- ✅ Africa: 20-meter resolution
- ✅ South America: 20-meter resolution
- ⏳ Rest of world: 30-meter resolution
  - Update to 20-meter expected later this year

## Common Issues and Solutions
1. **Edge Effects**: If you see suspicious linear patterns near tile edges, use the `edge_false_positives` filter
2. **Desert Areas**: Consider stricter soil moisture thresholds in arid regions
3. **Mountain Regions**: You may need to adjust `dem_metric_2` threshold based on your needs

## Known Limitations
- Detection quality may be reduced in urban areas and areas with dense vegetation cover
- While we try to control for false positives, certain soil types can still lead to false positives

## Citation
If you use this dataset, please cite our paper: [Paper citation to be added]

## Questions or Issues?
Please open an issue on our GitHub repository at https://github.com/microsoft/ai4g-flood or contact us at [amitmisra@microsoft.com]