Update README.md

README.md

@@ -3,57 +3,123 @@ 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 repository contains model output parquet files and geotiffs from our flood detection research. The data is organized in 3° × 3° tiles, with each tile containing both tabular data and processed geotiff images.
+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. In these cases, there aren't any geotiffs provided
+
+## 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
 ```
-├── N27                          # Main directory by latitude
-│   ├── N27E030                 # Subdirectory for specific tile
-│   │   ├── N27E030.parquet    # Tabular data
-│   │   ├── N27E030_90m.tif    # Geotiff with 90m buffer
-│   │   └── N27E030_240m.tif   # Geotiff with 240m buffer
+├── 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
 ```
 
-The naming convention uses the southwest corner coordinates of each tile. For example, `N27E030` represents:
-- Latitude: 27°N to 30°N
-- Longitude: 30°E to 33°E
-
-## Schema
-| Column | Type | Description |
-|--------|------|-------------|
-| year | int | Year of observation |
-| month | int | Month of observation |
-| day | int | Day of observation |
-| lat | float | Latitude of potential flood detection (pixel center) |
-| lon | float | Longitude of potential flood detection (pixel center) |
-| filename | str | Corresponding Sentinel-1 file name |
-| land_cover | int | ESA WorldCover land cover classification |
-| dem_metric_1 | float | Individual pixel slope |
-| dem_metric_2 | float | Maximum slope within 240m |
-| soil_moisture | float | LPRM algorithm absolute soil moisture (%) |
-| soil_moisture_zscore | float | Soil moisture anomaly (Oct 2014-Sep 2024 baseline) |
-| soil_moisture_sca | float | SCA algorithm absolute soil moisture (%) |
-| soil_moisture_sca_zscore | float | SCA soil moisture anomaly (Oct 2014-Sep 2024 baseline) |
-| temp | float | Average daily minimum temperature (1991-2020 climate) |
-| edge_false_positives | int | Edge effect detection flag |
+## 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
-For optimal results, we recommend applying the following filters:
+To reduce false positives, apply these filters:
 
 ```python
-filters = {
-    'dem_metric_2': '< 10',
-    'soil_moisture_sca': '> 1',
-    'soil_moisture_zscore': '> 1',
-    'soil_moisture': '> 20',
-    'temp': '> 0',
-    'land_cover': '!= 60',  # exclude bare ground
-    'edge_false_positives': '= 0'
+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 Status (Oct 31, 2024):
-  - Africa and South America: 20-meter resolution
-  - Rest of world: 30-meter resolution (20-meter update expected later this year)
+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 [[email protected]]