cog2h3 0.0.2

Cloud Optimized Geotiff to H3 Cell

This script processes Cloud Optimized GeoTIFFs (COGs) and converts them to H3 hexagonal grids, storing the results in a PostgreSQL database. It supports multi-band rasters, resampling, and various processing options.

Features

• Download and process COGs from URLs
• Convert raster data to H3 hexagons
• Support for multi-band rasters
• Customizable H3 resolution
• Various resampling methods
• Option to preserve raster value range during resampling
• Efficient database storage using PostgreSQL

Prerequisites

• Python 3.7+

• PostgreSQL database

• Ensure your input COG is in WGS 1984 projection. If not, reproject it using gdalwarp

Example :

gdalwarp -overwrite input.tif output.tif -s_srs EPSG:32645 -t_srs EPSG:4326

• Set nodata values to 0 using gdalwarp,

gdalwarp -dstnodata 0 input.tif output.tif

• Convert a GeoTIFF to COG format:

gdal_translate -of COG input.tif output_cog.tif

Environment Variables

Set the following environment variables or use default values:

• DATABASE_URL: PostgreSQL connection string (default: "postgresql://postgres:postgres@localhost:5432/postgres")
• STATIC_DIR: Directory to store downloaded COGs (default: "static")

Example:

export DATABASE_URL="postgresql://user:password@host:port/database"
export STATIC_DIR="/path/to/cog/storage"

Usage

Run the script with the following command:

python cog2h3.py --cog <COG_URL> --table <TABLE_NAME> [OPTIONS]

Required Arguments:

• --cog: URL of the Cloud Optimized GeoTIFF (must be in WGS84 projection)
• --table: Name of the database table to store results

Optional Arguments:

• --res: H3 resolution level (default: 8)
• --preserve_range: Preserve the value range of the raster while resampling
• --multiband: Process all bands of a multi-band raster
• --sample_by: Resampling method (default: "bilinear")

Available resampling methods: nearest, bilinear, cubic, cubic_spline, lanczos, average, mode, gauss, max, min, med, q1, q3, sum, rms

Example:

python cog2h3.py --cog my-cog.tif --table cog_h3 --res 8

Contribute

Contributions are Welcome ! स्वागतम