ENH: Add support for merging tiffs with different CRS #2697
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rouault
reviewed
Dec 30, 2022
| out_profile["nodata"] = nodata | ||
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| # create destination array | ||
| dest = np.zeros((output_count, output_height, output_width), dtype=dt) |
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This is an important implementation detail: the merge cannot deal with arbitrarily large datasets that wouldn't fit in RAM. Using GDALWarp() would avoid that limitation
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@VehpuS Thanks for the suggestion. Merging TIFFs with different CRS is by nature a reprojection/warping problem. Rasterio's merge is about combining data that is already aligned. Tiles, and that kind of thing. I don't want to blur the distinction. I agree that a tool to merge heterogenous collections of data would be useful, but I'm not eager to add this to Rasterio. Rasterio needs to focus on making low level raster data access better. |
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(Following #2696)
I have been looking into the implementation of rasterio.merge.merge to "prefetch" common bounds from a group of rasters. Once working on my implementation (since the function isn't currently too modular) I realized that I was getting errors when calculating common bounds of tiffs.
After some investigation, it turns out this was because the CRS of each tif was different, resulting in bounds given in different units. Before implementing my own solution, I looked at the merge implementation again but found no consideration for this. In the end, I implemented a solution based on rasterio.warp.transform_bounds.
I currently implemented this solution with a separate function since it may be a backward breaking change and I'm not sure whether that is something that would be appreciated. I replicated all existing tests for the new merge function (with support for multiple CRSs) but still need to add tests to check the feature specifically (i.e. actively try to merge tiffs with different CRSs).