Importing large raster datasets: Difference between revisions
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This is the method to use when creating downsampled versions of the main dataset (also called ''pyramids'' or ''overviews''). The default resampling method is average. The following table shows a benchmark comparing the different methods. Each row shows the percentage difference in execution time from <code>average</code> | This is the method to use when creating downsampled versions of the main dataset (also called ''pyramids'' or ''overviews''). The default resampling method is average. The following table shows a benchmark comparing the different methods. Each row shows the percentage difference in execution time from <code>average</code> | ||
{| class="wikitable" | {| class="wikitable" | ||
|+ | |+Resampling methods benchmark | ||
!Method | !Method | ||
!Average time (seconds) | |||
!Difference vs. baseline | !Difference vs. baseline | ||
!Sample tile | |||
|- | |- | ||
|average | |average | ||
|42.45 | |||
|0% | |0% | ||
|[[File:Average.jpg|frameless]] | |||
|- | |- | ||
|nearest | |nearest | ||
|38.50 | |||
| -9% | | -9% | ||
|[[File:Nearest.jpg|frameless]] | |||
|- | |- | ||
|bilinear | |bilinear | ||
|57.66 | |||
|36% | |36% | ||
|[[File:Bilinear.jpg|frameless]] | |||
|- | |- | ||
|cubic | |cubic | ||
|61.80 | |||
|46% | |46% | ||
|[[File:Cubic.jpg|frameless]] | |||
|- | |- | ||
|cubicspline | |cubicspline | ||
|60.64 | |||
|43% | |43% | ||
|[[File:Cubicspline.jpg|frameless]] | |||
|- | |- | ||
|lanczos | |lanczos | ||
|66.82 | |||
|57% | |57% | ||
|[[File:Lanczos.jpg|frameless]] | |||
|- | |- | ||
|gauss | |gauss | ||
|54.28 | |||
|28% | |28% | ||
|[[File:Gauss.jpg|frameless]] | |||
|} | |} | ||
Full article here: [[Comparing resampling methods in massivegeopackage]] | Full article here: [[Comparing resampling methods in massivegeopackage]] | ||
Revision as of 16:15, 8 September 2026
Importing raster datasets in the range of 10 GB or less can usually be done with the default Map Import settings. The general documentation on importing maps can be found here.
However, if the dataset you are importing is significantly larger - 20 GB or more - the settings become more important to get a good result without using too much processing time and file size. This article will go more in-depth on the different parameters and how they affect the import process and result.
Image Quality
Image quality in this context refers to how similar the output result is to the input data. This can be objectively measured as Structural Similarity (SSIM). We have run a benchmark comparing different compression methods and formats here.
Relevant Map Import parameters:
Resampling
This is the method to use when creating downsampled versions of the main dataset (also called pyramids or overviews). The default resampling method is average. The following table shows a benchmark comparing the different methods. Each row shows the percentage difference in execution time from average
| Method | Average time (seconds) | Difference vs. baseline | Sample tile |
|---|---|---|---|
| average | 42.45 | 0% | 
|
| nearest | 38.50 | -9% | 
|
| bilinear | 57.66 | 36% | 
|
| cubic | 61.80 | 46% | 
|
| cubicspline | 60.64 | 43% | 
|
| lanczos | 66.82 | 57% | 
|
| gauss | 54.28 | 28% | 
|
Full article here: Comparing resampling methods in massivegeopackage