Python-based CLI tool to index raster files to DGGS in parallel, writing out to Parquet.
This is the raster equivalent of vector2dggs.
Currently this supports the following DGGSs:
And these geocode systems:
Contributions (particularly for additional DGGSs), suggestions, bug reports and strongly worded letters are all welcome.
pip install raster2dggs
raster2dggs --help
Usage: raster2dggs [OPTIONS] COMMAND [ARGS]...
Options:
--version Show the version and exit.
--help Show this message and exit.
Commands:
a5 Ingest a raster image and index it to the A5 DGGS.
geohash Ingest a raster image and index it using the Geohash...
h3 Ingest a raster image and index it to the H3 DGGS.
maidenhead Ingest a raster image and index it using the Maidenhead...
rhp Ingest a raster image and index it to the rHEALPix DGGS.
s2 Ingest a raster image and index it to the S2 DGGS.
raster2dggs h3 --help
Usage: raster2dggs h3 [OPTIONS] RASTER_INPUT OUTPUT_DIRECTORY
Ingest a raster image and index it to the H3 DGGS.
RASTER_INPUT is the path to input raster data; prepend with protocol like
s3:// or hdfs:// for remote data. OUTPUT_DIRECTORY should be a directory,
not a file, as it will be the write location for an Apache Parquet data
store, with partitions equivalent to parent cells of target cells at a fixed
offset. However, this can also be remote (use the appropriate prefix, e.g.
s3://).
Options:
-v, --verbosity LVL Either CRITICAL, ERROR, WARNING, INFO or
DEBUG [default: INFO]
-r, --resolution [0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15]
H3 resolution to index [required]
-pr, --parent_res [0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15]
H3 parent resolution to index and aggregate
to. Defaults to resolution - 6
-b, --band TEXT Band(s) to include in the output. Can
specify multiple, e.g. `-b 1 -b 2 -b 4` for
bands 1, 2, and 4 (all unspecified bands are
ignored). If unused, all bands are included
in the output (this is the default
behaviour). Bands can be specified as
numeric indices (1-based indexing) or string
band labels (if present in the input), e.g.
-b B02 -b B07 -b B12.
-u, --upscale INTEGER Upscaling factor, used to upsample input
data on the fly; useful when the raster
resolution is lower than the target DGGS
resolution. Default (1) applies no
upscaling. The resampling method controls
interpolation. [default: 1]
-c, --compression TEXT Compression method to use for the output
Parquet files. Options include 'snappy',
'gzip', 'brotli', 'lz4', 'zstd', etc. Use
'none' for no compression. [default:
snappy]
-t, --threads INTEGER Number of threads to use when running in
parallel. The default is determined based
dynamically as the total number of available
cores, minus one. [default: 11]
-a, --aggfunc [count|mean|sum|prod|std|var|min|max|median|mode]
Numpy aggregate function to apply when
aggregating cell values after DGGS indexing,
in case of multiple pixels mapping to the
same DGGS cell. [default: mean]
-d, --decimals INTEGER Number of decimal places to round values
when aggregating. Use 0 for integer output.
[default: 1]
-o, --overwrite
--warp_mem_limit INTEGER Input raster may be warped to EPSG:4326 if
it is not already in this CRS. This setting
specifies the warp operation's memory limit
in MB. [default: 12000]
--resampling [nearest|bilinear|cubic|cubic_spline|lanczos|average|mode|gauss|max|min|med|q1|q3|sum|rms]
Input raster may be warped to EPSG:4326 if
it is not already in this CRS. Or, if the
upscale parameter is greater than 1, there
is a need to resample. This setting
specifies this resampling algorithm.
[default: average]
-co, --compact Compact the cells up to the parent
resolution. Compaction is not applied for
cells without identical values across all
bands.
--tempdir PATH Temporary data is created during the
execution of this program. This parameter
allows you to control where this data will
be written.
--version Show the version and exit.
--help Show this message and exit.
Output is in the Apache Parquet format, hive partitioned with the parent resolution as partition key. The example below is with -pr 3 with the H3 DGGS.
tree /home/user/example.pq
/home/user/example.pq
├── h3_03=83bb09fffffffff
│ └── part.0.parquet
└── h3_03=83bb0dfffffffff
└── part.0.parquetOutput can also be written to GeoParquet (v1.1.0) by including the -g/--geo parameter, which accepts:
polygonfor cells represented as boundary polygonspointfor cells represented as centre pointsnonefor standard Parquet output (not GeoParquet) ← this is the default if-g/--geois not used
GeoParquet output is useful if you want to use the spatial representations of the DGGS cells in traditional spatial analysis, or if you merely want to visualise the output.
Below are some ways to read and visualise it.
$ duckdb
DuckDB v1.4.1 (Andium) b390a7c376
Enter ".help" for usage hints.
Connected to a transient in-memory database.
Use ".open FILENAME" to reopen on a persistent database.
D INSTALL spatial;
D LOAD spatial;
D SELECT * FROM read_parquet('se_island.pq') LIMIT 7;
┌┌────────┬────────┬────────┬────────────────────────────────────────────────────────────────────────────────┬─────────────┬─────────┐
│ band_1 │ band_2 │ band_3 │ geometry │ s2_19 │ s2_08 │
│ float │ float │ float │ geometry │ varchar │ varchar │
├────────┼────────┼────────┼────────────────────────────────────────────────────────────────────────────────┼─────────────┼─────────┤
│ 0.0 │ 0.0 │ 0.0 │ POLYGON ((-176.17946725380486 -44.33542073938414, -176.17946725380486 -44.33… │ 72b47e01e24 │ 72b47 │
│ 0.0 │ 0.0 │ 0.0 │ POLYGON ((-176.18439390505398 -44.33543749229784, -176.18439390505398 -44.33… │ 72b47e02a14 │ 72b47 │
│ 0.0 │ 0.1 │ 0.1 │ POLYGON ((-176.18550630891403 -44.33547457195554, -176.18550630891403 -44.33… │ 72b47e1d54c │ 72b47 │
│ 0.0 │ 0.0 │ 0.0 │ POLYGON ((-176.17819578278952 -44.33537828938332, -176.17819578278952 -44.33… │ 72b47e01d64 │ 72b47 │
│ 0.1 │ 0.1 │ 0.3 │ POLYGON ((-176.18344039674218 -44.335553297533835, -176.18344039674218 -44.3… │ 72b47e0282c │ 72b47 │
│ 0.0 │ 0.0 │ 0.0 │ POLYGON ((-176.17899045588274 -44.335404822417665, -176.17899045588274 -44.3… │ 72b47e01dfc │ 72b47 │
│ 0.1 │ 0.1 │ 0.3 │ POLYGON ((-176.1832814769592 -44.33554799806149, -176.1832814769592 -44.3356… │ 72b47e02824 │ 72b47 │
└────────┴────────┴────────┴────────────────────────────────────────────────────────────────────────────────┴─────────────┴─────────┘ogrinfo -so -al ./se_island.pq
INFO: Open of `se_island.pq'
using driver `Parquet' successful.
Layer name: se_island
Geometry: Polygon
Feature Count: 18390
Extent: (-176.185824, -44.356933) - (-176.159915, -44.335364)
Layer SRS WKT:
GEOGCRS["WGS 84",
ENSEMBLE["World Geodetic System 1984 ensemble",
MEMBER["World Geodetic System 1984 (Transit)"],
MEMBER["World Geodetic System 1984 (G730)"],
MEMBER["World Geodetic System 1984 (G873)"],
MEMBER["World Geodetic System 1984 (G1150)"],
MEMBER["World Geodetic System 1984 (G1674)"],
MEMBER["World Geodetic System 1984 (G1762)"],
MEMBER["World Geodetic System 1984 (G2139)"],
MEMBER["World Geodetic System 1984 (G2296)"],
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ENSEMBLEACCURACY[2.0]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["geodetic latitude (Lat)",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["geodetic longitude (Lon)",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
USAGE[
SCOPE["Horizontal component of 3D system."],
AREA["World."],
BBOX[-90,-180,90,180]],
ID["EPSG",4326]]
Data axis to CRS axis mapping: 2,1
Geometry Column = geometry
band_1: Real(Float32) (0.0)
band_2: Real(Float32) (0.0)
band_3: Real(Float32) (0.0)
s2_19: String (0.0)
s2_08: String (0.0)qgis sample.pqWith some styling applied:
PyPi:
pip install raster2dggsConda environment:
name: raster2dggs
channels:
- conda-forge
channel_priority: strict
dependencies:
- python>=3.11,<3.12
- pip=23.1.*
- gdal>=3.8.5
- pyproj=3.6.*
- pip:
- raster2dggs>=0.6.0In brief, to get started:
- Install Poetry
- Install GDAL
- If you're on Windows,
pip install gdalmay be necessary before running the subsequent commands. - On Linux, install GDAL 3.6+ according to your platform-specific instructions, including development headers, i.e.
libgdal-dev.
- If you're on Windows,
- Create the virtual environment with
poetry init. This will install necessary dependencies. - Subsequently, the virtual environment can be re-activated with
poetry shell.
If you run poetry install, the CLI tool will be aliased so you can simply use raster2dggs rather than poetry run raster2dggs, which is the alternative if you do not poetry install.
Please run black . before committing.
Tests are included. To run them, set up a poetry environment, then follow these instructons:
cd tests
python ./test_raster2dggs.pyTest data are included at tests/data/.
Two sample files have been uploaded to an S3 bucket with s3:GetObject public permission.
s3://raster2dggs-test-data/Sen2_Test.tif(sample Sentinel 2 imagery, 10 bands, rectangular, Int16, LZW compression, ~10x10m pixels, 68.6 MB)s3://raster2dggs-test-data/TestDEM.tif(sample LiDAR-derived DEM, 1 band, irregular shape with null data, Float32, uncompressed, 10x10m pixels, 183.5 MB)
You may use these for experimentation. However you can also use local files too, which will be faster. A good, small (5 MB) sample image is available here.
A small test file is also available at [tests/data/se-island.tif] (tests/data/se-island.tif).
raster2dggs h3 --resolution 11 -d 0 s3://raster2dggs-test-data/Sen2_Test.tif ./tests/data/output/11/Sen2_Testraster2dggs rhp --resolution 11 -d 0 s3://raster2dggs-test-data/Sen2_Test.tif ./tests/data/output/11/Sen2_Test_rhpraster2dggs h3 --resolution 13 --compression zstd --resampling nearest -a median -d 1 -u 2 --geo polygon s3://raster2dggs-test-data/TestDEM.tif ./tests/data/output/13/TestDEM@software{raster2dggs,
title={{raster2dggs}},
author={Ardo, James and Law, Richard and Di Maio, Nicoletta},
url={https://github.com/manaakiwhenua/raster2dggs},
version={0.6.0},
date={2024-06-12}
}APA/Harvard
Ardo, J., Law, R., & Di Maio, N. (2025). raster2dggs (0.6.0) [Computer software]. https://github.com/manaakiwhenua/raster2dggs
