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handle raster2dggs conversion when raster uses projected CRS#57

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fmigneault wants to merge 1 commit intoopengeoshub:mainopengeoshub/vgrid:mainfrom
crim-ca:handle-raster2dggs-proj-crscrim-ca/vgrid:handle-raster2dggs-proj-crsCopy head branch name to clipboard
Open

handle raster2dggs conversion when raster uses projected CRS#57
fmigneault wants to merge 1 commit intoopengeoshub:mainopengeoshub/vgrid:mainfrom
crim-ca:handle-raster2dggs-proj-crscrim-ca/vgrid:handle-raster2dggs-proj-crsCopy head branch name to clipboard

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@fmigneault fmigneault commented Sep 1, 2025

The code currently assumes that Geographic CRS are employed. If the raster is using PROJ CRS, the result is an incoherent collection of cells spread around the world.

For example, using this image: https://rcm-ceos-ard.s3.ca-central-1.amazonaws.com/MLC/2025/08/20/RCM2_OK3556292_PK3758929_1_SC30MCPB_20250820_124729_CH_CV_MLC/RCM2_OK3556292_PK3758929_1_SC30MCPB_20250820_124729_RL.tif

The CRS is:

CRS.from_wkt("PROJCS["WGS 84 / UTM zone 14N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-99],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32614"]]")

The current code results in many random grid cells around the world.
(for example, one of the entry below is in Australia)

# before
transform * (col, row) = {tuple: 2} (619040.0, 5635760.0)
h3.latlng_to_cell(lat, lon, resolution) = {str} '889d5984b3fffff'
shapely.to_geojson(h32geo(h3_id)) = {str} '{
  "type" : "Polygon",
  "coordinates" : [ [ [ 139.99919040896353, -20.003853866867086 ], [ 140.00039675898358, -19.999125501107127 ], [ 139.9966348223183, -19.996016507198426 ], [ 139.99166664673348, -19.99763554491453 ], [ 139.99045993238093, -20.002363646094285 ], [ 139.99422175790926, -20.005472974146524 ], [ 139.99919040896353, -20.003853866867086 ] ] ]
}'

Now, it creates the correct grid positions in Canada (above Winnipeg, as expected).

transformer(transform * (col, row)) = {tuple: 2} (-97.30852836227533, 50.86126263102006)
h3.latlng_to_cell(lat, lon, resolution) = "882702880dfffff"
shapely.to_geojson(h32geo(h3_id)) = {str} '{
  "type" : "Polygon",
  "coordinates" : [ [ [ -97.31287367561494, 50.86284727324145 ], [ -97.31954080882947, 50.860269413001724 ], [ -97.3191299720562, 50.855273393469446 ], [ -97.31205368608208, 50.85285504796565 ], [ -97.30538710932244, 50.85543229263682 ], [ -97.30579626200928, 50.86042849831584 ], [ -97.31287367561494, 50.86284727324145 ] ] ]
}'
{A196EE5C-6375-41FF-9E16-EDB983EF4E30}

There are many other locations in the code that does similar assumptions, but I have only edited the raster conversion that were identical to the code I am invoking.

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