Develop Reference

get elevation from lat/long of geotiff data in gdal

I have a mosaic tif file (gdalinfo below) I made (with some additional info on the tiles here) and have looked extensively for a function that simply returns the elevation (the z value of this mosaic) for a given lat/long. The functions I've seen want me to input the coordinates in the coordinates of the mosaic, but I want to use lat/long, is there something about GetGeoTransform() that I'm missing to achieve this?
This example for instance here shown below:
from osgeo import gdal
import affine
import numpy as np
def retrieve_pixel_value(geo_coord, data_source):
"""Return floating-point value that corresponds to given point."""
x, y = geo_coord[0], geo_coord[1]
forward_transform = \
affine.Affine.from_gdal(*data_source.GetGeoTransform())
reverse_transform = ~forward_transform
px, py = reverse_transform * (x, y)
px, py = int(px + 0.5), int(py + 0.5)
pixel_coord = px, py
data_array = np.array(data_source.GetRasterBand(1).ReadAsArray())
return data_array[pixel_coord[0]][pixel_coord[1]]
This gives me an out of bounds error as it's likely expecting x/y coordinates (e.g. retrieve_pixel_value([153.023499,-27.468968],dataset). I've also tried the following from here:
import rasterio
dat = rasterio.open(fname)
z = dat.read()[0]
def getval(lon, lat):
idx = dat.index(lon, lat, precision=1E-6)
return dat.xy(*idx), z[idx]
Is there a simple adjustment I can make so my function can query the mosaic in lat/long coords?
Much appreciated.
Driver: GTiff/GeoTIFF
Files: mosaic.tif
Size is 25000, 29460
Coordinate System is:
PROJCRS["GDA94 / MGA zone 56",
BASEGEOGCRS["GDA94",
DATUM["Geocentric Datum of Australia 1994",
ELLIPSOID["GRS 1980",6378137,298.257222101004,
LENGTHUNIT["metre",1]],
ID["EPSG",6283]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433,
ID["EPSG",9122]]]],
CONVERSION["UTM zone 56S",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",0,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",153,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",0.9996,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",500000,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",10000000,
LENGTHUNIT["metre",1],
ID["EPSG",8807]],
ID["EPSG",17056]],
CS[Cartesian,2],
AXIS["easting",east,
ORDER[1],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]],
AXIS["northing",north,
ORDER[2],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]]]
Data axis to CRS axis mapping: 1,2
Origin = (491000.000000000000000,6977000.000000000000000)
Pixel Size = (1.000000000000000,-1.000000000000000)
Metadata:
AREA_OR_POINT=Area
Image Structure Metadata:
INTERLEAVE=BAND
Corner Coordinates:
Upper Left ( 491000.000, 6977000.000) (152d54'32.48"E, 27d19'48.33"S)
Lower Left ( 491000.000, 6947540.000) (152d54'31.69"E, 27d35'45.80"S)
Upper Right ( 516000.000, 6977000.000) (153d 9'42.27"E, 27d19'48.10"S)
Lower Right ( 516000.000, 6947540.000) (153d 9'43.66"E, 27d35'45.57"S)
Center ( 503500.000, 6962270.000) (153d 2' 7.52"E, 27d27'47.16"S)
Band 1 Block=25000x1 Type=Float32, ColorInterp=Gray
NoData Value=-999
Update 1 - I tried the following:
tif = r"mosaic.tif"
dataset = rio.open(tif)
d = dataset.read()[0]
def get_xy_coords(latlng):
transformer = Transformer.from_crs("epsg:4326", dataset.crs)
coords = [transformer.transform(x, y) for x,y in latlng][0]
#idx = dataset.index(coords[1], coords[0])
return coords #.xy(*idx), z[idx]
longx,laty = 153.023499,-27.468968
coords = get_elevation([(laty,longx)])
print(coords[0],coords[1])
print(dataset.width,dataset.height)
(502321.11181384244, 6961618.891167777)
25000 29460
So something is still not right. Maybe I need to subtract the coordinates from the bottom left/right of image e.g.
coords[0]-dataset.bounds.left,coords[1]-dataset.bounds.bottom
where
In [78]: dataset.bounds
Out[78]: BoundingBox(left=491000.0, bottom=6947540.0, right=516000.0, top=6977000.0)
Update 2 - Indeed, subtracting the corners of my box seems to get closer.. though I'm sure there is a much nice way just using the tif metadata to get what I want.
longx,laty = 152.94646, -27.463175
coords = get_xy_coords([(laty,longx)])
elevation = d[int(coords[1]-dataset.bounds.bottom),int(coords[0]-dataset.bounds.left)]
fig,ax = plt.subplots(figsize=(12,12))
ax.imshow(d,vmin=0,vmax=400,cmap='terrain',extent=[dataset.bounds.left,dataset.bounds.right,dataset.bounds.bottom,dataset.bounds.top])
ax.plot(coords[0],coords[1],'ko')
plt.show()

You basically have two distinct steps:
Convert lon/lat coordinates to map coordinates, this is only necessary if your input raster is not already in lon/lat. Map coordinates are the coordinates in the projection that the raster itself uses
Convert the map coordinates to pixel coordinates.
There are all kinds of tool you might use, perhaps to make things simpler (like pyproj, rasterio etc). But for such a simple case it's probably nice to start with doing it all in GDAL, that probably also enhances your understanding of what steps are needed.
Inputs
from osgeo import gdal, osr
raster_file = r'D:\somefile.tif'
lon = 153.023499
lat = -27.468968
lon/lat to map coordinates
# fetch metadata required for transformation
ds = gdal.OpenEx(raster_file)
raster_proj = ds.GetProjection()
gt = ds.GetGeoTransform()
ds = None # close file, could also keep it open till after reading
# coordinate transformation (lon/lat to map)
# define source projection
# this definition ensures the order is always lon/lat compared
# to EPSG:4326 for which it depends on the GDAL version (2 vs 3)
source_srs = osr.SpatialReference()
source_srs.ImportFromWkt(osr.GetUserInputAsWKT("urn:ogc:def:crs:OGC:1.3:CRS84"))
# define target projection based on the file
target_srs = osr.SpatialReference()
target_srs.ImportFromWkt(raster_proj)
# convert
ct = osr.CoordinateTransformation(source_srs, target_srs)
mapx, mapy, *_ = ct.TransformPoint(lon, lat)
You could verify this intermediate result by for example adding it as Point WKT in something like QGIS (using the QuickWKT plugin, making sure the viewer has the same projection as the raster).
map coordinates to pixel
# apply affine transformation to get pixel coordinates
gt_inv = gdal.InvGeoTransform(gt) # invert for map -> pixel
px, py = gdal.ApplyGeoTransform(gt_inv, mapx, mapy)
# it wil return fractional pixel coordinates, so convert to int
# before using them to read. Round to nearest with +0.5
py = int(py + 0.5)
px = int(px + 0.5)
# read pixel data
ds = gdal.OpenEx(raster_file) # open file again
elevation_value = ds.ReadAsArray(px, py, 1, 1)
ds = None
The elevation_value variable should be the value you're after. I would definitelly verify the result independently, try a few points in QGIS or the gdallocationinfo utility:
gdallocationinfo -l_srs "urn:ogc:def:crs:OGC:1.3:CRS84" filename.tif 153.023499 -27.468968
# Report:
# Location: (4228P,4840L)
# Band 1:
# Value: 1804.51879882812
If you're reading a lot of points, there will be some threshold at which it would be faster to read a large chunk and extract the values from that array, compared to reading every point individually.
edit:
For applying the same workflow on multiple points at once a few things change.
So for example having the inputs:
lats = np.array([-27.468968, -27.468968, -27.468968])
lons = np.array([153.023499, 153.023499, 153.023499])
The coordinate transformation needs to use ct.TransformPoints instead of ct.TransformPoint which also requires the coordinates to be stacked in a single array of shape [n_points, 2]:
coords = np.stack([lons.ravel(), lats.ravel()], axis=1)
mapx, mapy, *_ = np.asarray(ct.TransformPoints(coords)).T
# reshape in case of non-1D inputs
mapx = mapx.reshape(lons.shape)
mapy = mapy.reshape(lons.shape)
Converting from map to pixel coordinates changes because the GDAL method for this only takes single point. But manually doing this on the arrays would be:
px = gt_inv[0] + mapx * gt_inv[1] + mapy * gt_inv[2]
py = gt_inv[3] + mapx * gt_inv[4] + mapy * gt_inv[5]
And rounding the arrays to integer changes to:
px = (px + 0.5).astype(np.int32)
py = (py + 0.5).astype(np.int32)
If the raster (easily) fits in memory, reading all points would become:
ds = gdal.OpenEx(raster_file)
all_elevation_data = ds.ReadAsArray()
ds = None
elevation_values = all_elevation_data[py, px]
That last step could be optimized by checking highest/lowest pixel coordinates in both dimensions and only read that subset for example, but it would require normalizing the coordinates again to be valid for that subset.
The py and px arrays might also need to be clipped (eg np.clip) if the input coordinates fall outside the raster. In that case the pixel coordinates will be < 0 or >= xsize/ysize.

I keep getting blank images while trying to Georeference an image with GDAL

#!/usr/bin/env python3
import numpy as np
from osgeo import gdal
from osgeo import osr
# Load an array with shape (197, 250, 3)
# Data with dim of 3 contain (value, longitude, latitude)
data = np.load("data.npy")
# Copy the data and coordinates
array = data[:,:,0]
lon = data[:,:,1]
lat = data[:,:,2]
nLons = array.shape[1]
nLats = array.shape[0]
# Calculate the geotransform parameters
maxLon, minLon, maxLat, minLat = [lon.max(), lon.min(), lat.max(), lat.min()]
resLon = (maxLon - minLon) / nLons
resLat = (maxLat - minLat) / nLats
# Get the transform
geotransform = (minLon, resLon, 0, maxLat, 0, -resLat)
# Create the ouptut raster
output_raster = gdal.GetDriverByName('GTiff').Create('myRaster.tif', nLons, nLats, 1,
gdal.GDT_Int32)
# Set the geotransform
output_raster.SetGeoTransform(geotransform)
srs = osr.SpatialReference()
# Set to world projection 4326
srs.ImportFromEPSG(4326)
output_raster.SetProjection(srs.ExportToWkt())
output_raster.GetRasterBand(1).WriteArray(array)
output_raster.FlushCache()
The code above is meant to georeference a raster using GDAL but returns blank tiff files. I have vetted the data and variables, I, however, suspect the problem could be from geotransform variables. The documentation demands the variable to be:
top-left-x, w-e-pixel-resolution, 0,
top-left-y, 0, n-s-pixel-resolution (negative value)
I have used lats and lons not sure I'm getting which one corresponds to x and which to y. It could be something else but I'm not quite sure.

Overall your approach looks correct to me, but it's hard to tell without seeing the data you're using, but here are some points to consider:
First, there's a difference between the output file being empty, and/or being in the wrong location, georeferencing relates only to the latter.
When working interactive, you should also make sure to properly close the Dataset using output_raster = None, that will also trigger flushing for you.
You could start by testing if GDAL reads the same data that you intended to write. Using something like:
ds = gdal.Open('myRaster.tif')
data_from_disk = ds.ReadAsArray()
ds = None
np.testing.assert_array_equal(data_from_disk, array)
If those are not identical, it could be an issue with the datatype. Like writing floats close to 0 as integers, causing them to clip to 0 giving the appearance of an "empty" file.
Regarding the georeferencing, the projection you use has the coordinates in degrees. If yours are in radians your output ends up close to null-island.
Your approach also assumes that the data and lat/lon arrays are on a regular grid (having a constant resolution). That might not be the case (especially if the data comes with a 2D grid of coordinates).
Often when coordinate arrays are given, they are defined as valid for the center of the pixel. Compared to GDAL's geotransform which is defined for the (outer) edge of the pixel. So you might need to account for that by subtracting half the resolution. And this also impacts your calculation of the resolution, which in the case for the center-definition should probably use / (nLons-1) & / (nLats-1). Or alternatively verify with:
# for a regular grid
resLon = lon[0,1] - lon[0,0]
resLat = lat[1,0] - lat[0,0]
When I run your snippet with some dummy data, it gives me a correct output (ignoring the center/edge issue mentioned above).
lat, lon = np.mgrid[89:-90:-2, -179:180:2]
array = np.sqrt(lon**2 + lat**2).astype(np.int32)

Coordinate system used for healpy.query_disc()

The healpy.query_disc() function takes an argument vec which is a three-component unit vector defining the center of the disc. What coordinate system is being used here - why is there a third dimension for a 2-d projection? What point is the "tail" of this vector llocated at?

Very good you found the solution yourself, for later reference here is a full working code example:
import healpy as hp
import numpy as np
# `lonlat=True` switches `ang2vec` from requiring colatitude $\theta$ and longitude $\phi$ in radians to longitude and latitude in degrees (notice that also the order changes)
# in degrees
lon = 60
lat = 30
vec = hp.ang2vec(lon, lat, lonlat=True)
nside = 256
large_disc = hp.query_disc(nside, vec, radius=np.radians(20))
small_disc = hp.query_disc(nside, vec, radius=np.radians(8))
tiny_disc = hp.query_disc(nside, vec, radius=np.radians(2))
# `query_disc` returns a list of pixels, by default in RING ordering, let's check their length:
list(map(len, [large_disc, small_disc, tiny_disc]))
# ## Create a map and plot it in Mollweide projection
m = np.zeros(hp.nside2npix(nside))
m[large_disc] = 1
m[small_disc] = 2
m[tiny_disc] = 3
hp.mollview(m)
hp.graticule()
See the notebook with plots here: https://zonca.dev/2020/10/example-healpy-query_disc.html

Solved. Used the output of ang2vec() to give the vector.

Find indices of raster cells that intersect with a polygon

I want to get a list of indices (row,col) for all raster cells that fall within or are intersected by a polygon feature. Looking for a solution in python, ideally with gdal/ogr modules.
Other posts have suggested rasterizing the polygon, but I would rather have direct access to the cell indices if possible.

Since you don't provide a working example, it's bit unclear what your starting point is. I made a dataset with 1 polygon, if you have a dataset with multiple but only want to target a specific polygon you can add SQLStatement or where to the gdal.Rasterize call.
Sample polygon
geojson = """{"type":"FeatureCollection",
"name":"test",
"crs":{"type":"name","properties":{"name":"urn:ogc:def:crs:OGC:1.3:CRS84"}},
"features":[
{"type":"Feature","properties":{},"geometry":{"type":"MultiPolygon","coordinates":[[[[-110.254,44.915],[-114.176,37.644],[-105.729,36.41],[-105.05,43.318],[-110.254,44.915]]]]}}
]}"""
Rasterizing
Rasterizing can be done with gdal.Rasterize. You need to specify the properties of the target grid. If there is no predefined grid these could be extracted from the polygon itself
ds = gdal.Rasterize('/vsimem/tmpfile', geojson, xRes=1, yRes=-1, allTouched=True,
outputBounds=[-120, 30, -100, 50], burnValues=1,
outputType=gdal.GDT_Byte)
mask = ds.ReadAsArray()
ds = None
gdal.Unlink('/vsimem/tmpfile')
Converting to indices
Retrieving the indices from the rasterized polygon can be done with Numpy:
y_ind, x_ind = np.where(mask==1)

Clearly Rutger's solution above is the way to go with this, however I will leave my solution up. I developed a script that accomplished what I needed with the following:
Get the bounding box for each vector feature I want to check
Use the bounding box to limit the computational window (determine what portion of the raster could potentially have intersections)
Iterate over the cells within this part of the raster and construct a polygon geometry for each cell
Use ogr.Geometry.Intersects() to check if the cell intersects with the polygon feature
Note that I have only defined the methods, but I think implementation should be pretty clear -- just call match_cells with the appropriate arguments (ogr.Geometry object and geotransform matrix). Code below:
from osgeo import ogr
# Convert projected coordinates to raster cell indices
def parse_coords(x,y,gt):
row,col = None,None
if x:
col = int((x - gt[0]) // gt[1])
# If only x coordinate is provided, return column index
if not y:
return col
if y:
row = int((y - gt[3]) // gt[5])
# If only x coordinate is provided, return column index
if not x:
return row
return (row,col)
# Construct polygon geometry from raster cell
def build_cell((row,col),gt):
xres,yres = gt[1],gt[5]
x_0,y_0 = gt[0],gt[3]
top = (yres*row) + y_0
bottom = (yres*(row+1)) + y_0
right = (xres*col) + x_0
left = (xres*(col+1)) + x_0
# Create ring topology
ring = ogr.Geometry(ogr.wkbLinearRing)
ring.AddPoint(left,bottom)
ring.AddPoint(right,bottom)
ring.AddPoint(right,top)
ring.AddPoint(left,top)
ring.AddPoint(left,bottom)
# Create polygon
box = ogr.Geometry(ogr.wkbPolygon)
box.AddGeometry(ring)
return box
# Iterate over feature geometries & check for intersection
def match_cells(inputGeometry,gt):
matched_cells = []
for f,feature in enumerate(inputGeometry):
geom = feature.GetGeometryRef()
bbox = geom.GetEnvelope()
xmin,xmax = [parse_coords(x,None,gt) for x in bbox[:2]]
ymin,ymax = [parse_coords(None,y,gt) for y in bbox[2:]]
for cell_row in range(ymax,ymin+1):
for cell_col in range(xmin,xmax+1):
cell_box = build_cell((cell_row,cell_col),gt)
if cell_box.Intersects(geom):
matched_cells += [[(cell_row,cell_col)]]
return matched_cells

if you want to do this manually you'll need to test each cell for:
Square v Polygon intersection and
Square v Line intersection.
If you treat each square as a 2d point this becomes easier - it's now a Point v Polygon problem. Check in Game Dev forums for collision algorithms.
Good luck!

Sample raster cell value at coordinates?

I have many rasters in ESRI ASCII format (http://resources.arcgis.com/en/help/main/10.1/index.html#/Esri_ASCII_raster_format/009t0000000z000000/).
I need to extract cell values at given locations / coordinates. Can anyone suggest a python package to achieve this? I suspect there may be something in gdal tools but I have been unable to find anything so far.
I am looking for similar functionality to GMT grdtrack, with which you can pass a table of coordinates and retrieve the cell value.
http://gmt.soest.hawaii.edu/doc/5.1.0/grdtrack.html
However I was hoping / wondering if this is possible in python as my previous and later stages of my analysis are all in python.

Of course, you can do this with GDAL. Provided that you have the coordinate where you want to sample your raster in the same projection you can do something like this:
from osgeo import gdal
def world2Pixel(gt, x, y):
ulX = gt[0]
ulY = gt[3]
xDist = gt[1]
yDist = gt[5]
rtnX = gt[2]
rtnY = gt[4]
pixel = int((x - ulX) / xDist)
line = int((ulY - y) / yDist)
return (pixel, line)
dataset = gdal.Open(filename)
gt = dataset.GetGeoTransform()
pixel, line = world2Pixel(gt, x, y)
band = dataset.GetRasterBand(1)
value = band.ReadAsArray(pixel, line, 1, 1)[0, 0]