Byte2Bit - Data Compression Solutions for Mission-Critical Systems

Storage Reduction

60%

On geospatial data

Compression

Lossless

Bit-for-bit reconstruction

NOAA GFS

52%

2.5 GB → 1.2 GB

ERA5 Zarr

37%

Better than Zstd

Formats

GRIB, NetCDF, COG…

GDAL Plugin

Ready

Drop-in codec

Geospatial & Scientific Data Products

Purpose-built compression for EO, weather, and raster workflows

Atlas

Data Compression & Access

Lossless compression for GRIB, NetCDF, Zarr, HDF5, and Cloud-Optimized GeoTIFF. Cut storage 60% while keeping random-access, chunk-level querying fast.

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Prism

Satellite & Hyperspectral Imagery

Hyperspectral and multispectral image compression with 30–50% better efficiency than CCSDS/JPEG-LS. Random access for instant spatial and spectral retrieval.

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GDAL Plugin

Developer Tool

Use Byte2Bit as a compression codec inside GDAL. Drop-in support for Cloud-Optimized GeoTIFF, NetCDF, Zarr, and raster workflows with no pipeline changes needed.

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Geospatial Data Pipeline: Where Byte2Bit Fits

Click each stage to see how compression reduces costs from sensor to delivery

Raw data generated from satellites, UAVs, radar, ground sensors, and NWP models in GRIB2, NetCDF, HDF5, and GeoTIFF formats.

Get Started in Minutes

Install the Byte2Bit Atlas SDK or GDAL plugin and start compressing geospatial data immediately. Works as a drop-in layer in GDAL, QGIS, Rasterio, and Python pipelines with no workflow changes needed.

GRIB2NetCDFZarrHDF5Cloud-Optimized GeoTIFFNumPyLiDAR (LAS/LAZ)

terminal

# Install Byte2Bit Atlas

$ pip install byte2bit-atlas

# Compress a NOAA GFS forecast

>>> import byte2bitZarr as b2b

>>> b2b.transform('gfs_0p25.grib2', 'gfs_0p25.b2b')

>>> b2b.verify('gfs_0p25.grib2', 'gfs_0p25.b2b')

✓ Lossless verification passed

✓ 52% storage reduction (2.50 GB → 1.19 GB)

✓ Random-access read: 8 ms per chunk

We tested Byte2Bit's lossless compression on raw global weather data across multiple variables, benchmarking against zlib and other open-source alternatives. Byte2Bit achieved up to 20% better compression ratios while improving decompression performance significantly. Based on our results, it outperforms any open-source solution we are aware of, offering a superior combination of storage reduction and efficient data retrieval for large-scale scientific datasets.

Lukas Hedegaard Morsing

Machine Learning Scientist, InCommodities

Tested on NOAA GFS ERA5 Zarr benchmarked DAS sensor data verified

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