rioxarray
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corteva
Distortion in output geotiffs for non-contiguous blocks of geospatial data
Problem description
Attached data : clusters.csv
I have a geospatial dataset, a single csv containing x, y and clusters data (about 147 000 cells). The cell size is 10m and the CRS is epsg:26911. The dataset comprises two distinct yet nearby areas :
I want to convert this csv data to geotiff by using rioxarray.to_raster function.
Expected Output
I would expect the raster to look like this
I rather get a raster where the cells are slightly offset from where they should be. The cell size is now 10.48xxxxx m when I was expecting a 10 m. See below on the image, the black transparent layer is the problematic one created with rioxarray while the ''correct'' colored one is the output created with geocube.
I found that this behavior happens only when you have two or more distrinct / non-contiguous spatial blocks in your data. Otherwise, if the data is in a single block (cell that touches), the output is fine.
Code
import xarray
import rioxarray as rio
import time
import pandas as pd
def test_csv_to_raster(input_csv, output_directory, columns, x_field, y_field, dstCRS):
start_time = time.time()
df = pd.read_csv(input_csv)
df.rename(columns={x_field: 'x', y_field: 'y'}, inplace = True)
df.set_index(['x', 'y'], inplace=True)
xr = xarray.Dataset.from_dataframe(df)
xr = xr.rio.set_spatial_dims('x', 'y')
xr = xr.rio.write_crs(dstCRS)
xr = xr.transpose('y', 'x')
xr = xr.fillna(-9999)
if (os.path.exists(output_directory) != True):
os.makedirs(output_directory)
print("Rasterizing...")
for col in columns:
print("Column : " + col)
output_path = os.path.join(output_directory, col + ".tif")
xr[col] = xr[col].astype(dtype='int16')
xr[col] = xr[col].where(xr[col] != -9999)
xr[col].rio.to_raster(raster_path=output_path, nodata=-9999)
print("Done. See file " + output_path)
end_time = time.time()
print("Completed. Time elapsed (s) : ")
print(end_time - start_time)
return
cl_start = 5
cl_end = 30
crs='epsg:26911'
input_csv = 'clusters.csv'
columns = [f'labels_n{i}' for i in range(cl_start, cl_end+1)]
output_directory = 'my_rasters/'
test_csv_to_raster(input_csv, output_directory, columns, 'x', 'y', crs)
Environment Information
I use QGIS for visualisation of the geotiffs.
Installation method
conda
Conda environment information (if you installed with conda):
Environment (
conda list):
$ # packages in environment at C:\Users\Utilisateur\.conda\envs\geods37:
#
# Name Version Build Channel
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argcomplete 1.12.3 pyhd8ed1ab_2 conda-forge
argon2-cffi 21.1.0 py37hcc03f2d_0 conda-forge
arrow-cpp 6.0.0 py37h88b90bb_3_cpu conda-forge
asciitree 0.3.3 py_2
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aws-c-common 0.6.11 h8ffe710_0 conda-forge
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aws-c-event-stream 0.2.7 he02faa2_24 conda-forge
aws-c-http 0.6.6 h77bbe4a_1 conda-forge
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umap-learn 0.5.1 py37h03978a9_1 conda-forge
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vc 14.2 hb210afc_5 conda-forge
vs2015_runtime 14.29.30037 h902a5da_5 conda-forge
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xerces-c 3.2.3 h0e60522_2 conda-forge
xgboost 1.5.0 py37h03978a9_0 conda-forge
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zict 2.0.0 py_0 conda-forge
zipp 3.6.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.11 h8ffe710_1013 conda-forge
zstd 1.5.0 h6255e5f_0 conda-forge
Details about
conda and system ( conda info ):
$ conda info
active environment : geods37
active env location : C:\Users\Utilisateur\.conda\envs\geods37
shell level : 2
user config file : C:\Users\Utilisateur\.condarc
populated config files : C:\Users\Utilisateur\.condarc
conda version : 4.10.0
conda-build version : 3.20.5
python version : 3.8.5.final.0
virtual packages : __cuda=11.5=0
__win=0=0
__archspec=1=x86_64
base environment : C:\ProgramData\Anaconda3 (read only)
conda av data dir : C:\ProgramData\Anaconda3\etc\conda
conda av metadata url : https://repo.anaconda.com/pkgs/main
channel URLs : https://repo.anaconda.com/pkgs/main/win-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/win-64
https://repo.anaconda.com/pkgs/r/noarch
https://repo.anaconda.com/pkgs/msys2/win-64
https://repo.anaconda.com/pkgs/msys2/noarch
package cache : C:\ProgramData\Anaconda3\pkgs
C:\Users\Utilisateur\.conda\pkgs
C:\Users\Utilisateur\AppData\Local\conda\conda\pkgs
envs directories : C:\Users\Utilisateur\.conda\envs
C:\ProgramData\Anaconda3\envs
C:\Users\Utilisateur\AppData\Local\conda\conda\envs
platform : win-64
user-agent : conda/4.10.0 requests/2.24.0 CPython/3.8.5 Windows/10 Windows/10.0.19041
administrator : False
netrc file : None
offline mode : False
Thanks a lot, P-H
I looked at your data, and it appears that your points don't properly represent the grid.
If I do:
numpy.unique(xr.y.diff(dim="y"))
I get:
array([ 10., 710.])
To solve your problem, I believe you need to rasterize your points to a grid. geocube can help: https://corteva.github.io/geocube/stable/getting_started.html
For example:
gdf = geopandas.GeoDataFrame(
df[columns],
geometry=geopandas.points_from_xy(
df.x, df.y, crs="epsg:26911"
)
)
xr = make_geocube(
gdf,
resolution=10,
fill=-9999,
)
for col in columns:
print("Column : " + col)
output_path = os.path.join(output_directory, col + ".tif")
xr[col].rio.to_raster(raster_path=output_path, dtype="int16")
Thanks @snowman2,
My group and I usually work with contiguous blocks of data, but it happens that sometimes we have to deal with non-contiguous data such as what I am showing here.
When applying the make_geocube() command, the output geotiffs are correct. So I understand that rioxarray requires that the x-y points are evenly spaced ? Is that right?
However, it seems that the execution time increases by lot (from 2-3s to 90s). Is there a workaround solution that would take less computation time ?
So I understand that rioxarray requires that the x-y points are evenly spaced ? Is that right?
That is correct.
Is there a workaround solution that would take less computation time ?
You would need to add in the missing rows filled in with nodata.
I am closing as I believe this has been resolved. If you have further questions, feel free to ask them.