| Name | cog2h3 JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | None |
| Summary | Command line script to convert Cloud Optimized Geotiff to h3 cells and insert to PosgreSQL |
| upload_time | 2024-08-22 07:24:41 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | GPL-3.0-only |
| keywords |
h3
index
postgresql
cog
tiff
raster
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
### 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
```shell
gdalwarp -overwrite input.tif output.tif -s_srs EPSG:32645 -t_srs EPSG:4326
```
- Set nodata values to 0 using `gdalwarp`,
```shell
gdalwarp -dstnodata 0 input.tif output.tif
```
- Convert a GeoTIFF to COG format:
```shell
gdal_translate -of COG input.tif output_cog.tif
```
**This all preprocessing step is included in `./pre.sh` bash script**
## 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:
```shell
export DATABASE_URL="postgresql://user:password@host:port/database"
export STATIC_DIR="/path/to/cog/storage"
```
## Usage
Run the script with the following command:
```shell
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:
```shell
python cog2h3.py --cog my-cog.tif --table cog_h3 --res 8
```
## Contribute
Contributions are Welcome ! स्वागतम
Raw data
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