metadata

license: mit

Flood Detection Dataset

Quick Start

# 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?

Parquet file (post-processing.parquet): Contains detailed observations with timestamps, locations, and environmental metrics
90-meter buffer geotiff (90m-buffer.tif): Filtered flood extent with 90m safety buffer
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

Identify the coordinates of your area
Round down to the nearest 3 degrees for both latitude and longitude
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:

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

Edge Effects: If you see suspicious linear patterns near tile edges, use the edge_false_positives filter
Desert Areas: Consider stricter soil moisture thresholds in arid regions
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]