How can I obtain projected coordinates as well as the actual pixel values at those coordinates from a GeoTiff file and then save them into a numpy array? I have arsenci020l.tif file, and its coordinates are in meters. Below is the abridged output of gdalinfo I ran on it.

~$ gdalinfo arsenci020l.tif 
Driver: GTiff/GeoTIFF
Files: arsenci020l.tif
Size is 10366, 7273
Coordinate System is:
PROJCS["Lambert Azimuthal Equal Area projection with arbitrary plane grid; projection center 100.0 degrees W, 45.0 degrees N",
    GEOGCS["WGS 84",
            SPHEROID["WGS 84",6378137,298.257223563,
Origin = (-6086629.000000000000000,4488761.000000000000000)
Pixel Size = (1000.000000000000000,-1000.000000000000000)

There was a similar question here about obtaining lat/long coordinates from tiff (Obtain Latitude and Longitude from a GeoTIFF File) and the answer showed how to obtain only top left x and y pixel coordinates. I need to obtain ALL projected pixel coordinates as well as get the pixel values and save them in a numpy array. How can I do it?

  • You want 10366 × 7273 = over 75 million points?
    – Mike T
    Commented Jan 11, 2015 at 23:39
  • @MikeT I think so,I don't really know of a better solution of how to approach the problem I'm trying to solve:I need to find the closest pixel coordinate from this dataset to each centroid of US block and then assign the corresponding pixel value to that block.From searching around I realized that cKDTree query is going to help me with nearest neighbor search.Python function for the algorithm asks for a "tree" to query as numpy array.In order to make a "tree" out of all pixel coordinates from this dataset,I need to store all of them somehow.If you have a better solution,please let me know!
    – irakhman
    Commented Jan 11, 2015 at 23:55
  • I cannot understand why this procedure requires so much code in Python.
    – geotheory
    Commented Jul 11, 2020 at 15:45

2 Answers 2


This should get you going. The raster values are read using rasterio, and pixel centre coordinates are converted to Eastings/Northings using affine, which are then converted to Latitude/Longitude using pyproj. Most arrays have the same shape as the input raster.

import rasterio
import numpy as np
from affine import Affine
from pyproj import Proj, transform

fname = '/path/to/your/raster.tif'

# Read raster
with rasterio.open(fname) as r:
    T0 = r.transform  # upper-left pixel corner affine transform
    p1 = Proj(r.crs)
    A = r.read()  # pixel values

# All rows and columns
cols, rows = np.meshgrid(np.arange(A.shape[2]), np.arange(A.shape[1]))

# Get affine transform for pixel centres
T1 = T0 * Affine.translation(0.5, 0.5)
# Function to convert pixel row/column index (from 0) to easting/northing at centre
rc2en = lambda r, c: (c, r) * T1

# All eastings and northings (there is probably a faster way to do this)
eastings, northings = np.vectorize(rc2en, otypes=[float, float])(rows, cols)

# Project all longitudes, latitudes
p2 = Proj(proj='latlong',datum='WGS84')
longs, lats = transform(p1, p2, eastings, northings)
  • 1
    When using this, I get the message "AttributeError: 'DatasetReader' object has no attribute 'affine'" for the line "T0 = r.affine"
    – mitchus
    Commented Aug 31, 2018 at 10:05
  • @mitchus Apparently affine is just an alias for transform, and the alias has been removed from the most recent version of rasterio. I edited the answer but it looks like it needs to be peer-reviewed since I'm new here. :)
    – Translunar
    Commented Feb 26, 2019 at 16:36
  • 1
    It also looks like the indexes are wrong for A.shape, which has only two dimensions.
    – Translunar
    Commented Feb 26, 2019 at 16:38
  • 1
    rasterio pads a 1 channel dimension so that shape is always (channel, row, col)
    – bw4sz
    Commented Jul 5, 2020 at 2:53

would add as comment, but a bit long - in case you wanted to use gdal/ogr within python - something like this might work (hacked together from some other code i had - not tested!) This also assumes that rather than finding the nearest raster pixel to a polygon centroid, you simply query the raster at the xy of the centroid. i have no idea what the speed tradeoff might be...

from osgeo import gdal,ogr


def GetCentroidValue(fc,rast):
    #open vector layer
    drv=ogr.GetDriverByName('ESRI Shapefile') #assuming shapefile?
    ds=drv.Open(fc,True) #open for editing

    #open raster layer
    gdal.UseExceptions() #so it doesn't print to screen everytime point is outside grid

    for feat in lyr:

        px = int((mx - gt[0]) / gt[1]) #x pixel
        py = int((my - gt[3]) / gt[5]) #y pixel
        try: #in case raster isnt full extent
            structval=rb.ReadRaster(px,py,1,1,buf_type=gdal.GDT_Float32) #Assumes 32 bit int- 'float'
            intval = struct.unpack('f' , structval) #assume float
            val=-9999 #or some value to indicate a fail




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