#LZW DECOMPRESSOR DOWNLOAD DOWNLOAD#
See the notes and comments section for a download link and more information. Each file has been cropped to be around 50Mb each in its uncompressed condition. Three test files with commonly used data types have been created for this test: byte.tif, int16.tif, and float32.tif. We're going to run a benchmark to test compression ratio and read/write speeds for various data types and algorithms with their respective configuration options. With the fast ZSTD compression (GDAL 2.3 and above) and Limited Error Raster Compression (GDAL 2.4) becoming available in our favourite geospatial toolkit, I thought it would be interesting to run some benchmarks and write a guide on compressing and optimizing GeoTIFF files using the latest versions of GDAL.Įspecially if you're working with GDAL's virtual file systems and cloud-optimized GeoTIFFs, deciding on the right compression algorithm and creation options can make a significant difference to indicators such as file size, processing time, and the amount of time and bandwidth consumed when accessing geospatial data over a network.
Check it out at or follow on Twitter for news and updates. It contains lots of maps and charts, all powered by open data. Shameless plug ahead! This article was (probably) written in the course of building Geofolio, which is a project I've been working on that automagically generates factsheets for any place on Earth.
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