ai-for-good-lab/ai4g-flood-dataset

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?

1. Parquet file (post-processing.parquet): Contains detailed observations with timestamps, locations, and environmental metrics
2. 80-meter buffer geotiff (80m-buffer.tif): Filtered flood extent with 80m safety buffer
3. 240-meter buffer geotiff (240m-buffer.tif): Filtered flood extent with wider 240m safety buffer
4. Flood recurrence geotiff with 80-meter buffer (recurrence-80m-buffer.tif): Number of distinct months with flooding detected.

In the geotiffs:

• Value 2: Pixels with flooding detected within the buffer distance (80m or 240m). For the recurrence file, it is number of months with flooding minus 1. So 2=1 month of flooding, 3=2 months of flooding, etc.
• Value 1: Default exclusion layer representing areas with potential false positives (rough terrain or arid regions) or false negatives (urban areas)
• Value 0: Areas without any flood detection and outside of our exclusion mask

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:

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 Feb 24,2025):

• ✅ Global geotiffs: 20-meter resolution
• ✅ Africa parquet files: 20-meter resolution
• ⏳ Rest of world parquet files: 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: https://arxiv.org/abs/2411.01411

Questions or Issues?

Please open an issue on our GitHub repository at https://github.com/microsoft/ai4g-flood or contact us at [[email protected]]