Topographic Map

This is very similar to the Applying Textures example except it is focused on plotting aerial imagery from a GeoTIFF on top of some topography mesh.

# sphinx_gallery_thumbnail_number = 4

import pyvista as pv
from pyvista import examples

# Load the elevation data as a surface
elevation = examples.download_crater_topo().warp_by_scalar()
# Load the topographic map from a GeoTiff
topo_map = examples.download_crater_imagery()

elevation
HeaderData Arrays
StructuredGridInformation
N Cells1677401
N Points1680000
X Bounds1.810e+06, 1.831e+06
Y Bounds5.640e+06, 5.658e+06
Z Bounds7.339e+02, 2.787e+03
Dimensions1400, 1200, 1
N Arrays1
NameFieldTypeN CompMinMax
scalar1of1Pointsfloat6417.339e+022.787e+03


Let’s inspect the imagery that we just loaded

import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 500

plt.imshow(topo_map.to_array())
topo map

Out:

<matplotlib.image.AxesImage object at 0x7f210c0bab20>

Once you have a topography mesh loaded as a surface mesh (we use a pyvista.StructuredGrid here) and an image loaded as a pyvista.Texture object using the pyvista.read_texture() method, then you can map that imagery to the surface mesh as follows:

# Bounds of the aerial imagery - given to us
bounds = (1818000, 1824500, 5645000, 5652500, 0, 3000)
# Clip the elevation dataset to the map's extent
local = elevation.clip_box(bounds, invert=False)
# Apply texturing coordinates to associate the image to the surface
local.texture_map_to_plane(use_bounds=True, inplace=True)
HeaderData Arrays
UnstructuredGridInformation
N Cells436733
N Points222110
X Bounds1.818e+06, 1.825e+06
Y Bounds5.645e+06, 5.653e+06
Z Bounds1.381e+03, 2.787e+03
N Arrays2
NameFieldTypeN CompMinMax
scalar1of1Pointsfloat6411.381e+032.787e+03
Texture CoordinatesPointsfloat3220.000e+001.000e+00


Now display it! Note that the imagery is aligned as we expect.

local.plot(texture=topo_map, cpos="xy")
topo map

And here is a 3D perspective!

local.plot(texture=topo_map)
topo map

We could also display the entire region by extracting the surrounding region and plotting the texture mapped local topography and the outside area

# Extract surrounding region from elevation data
surrounding = elevation.clip_box(bounds, invert=True)

# Display with a shading technique
p = pv.Plotter()
p.add_mesh(local, texture=topo_map)
p.add_mesh(surrounding, color="white")
p.enable_eye_dome_lighting()
p.camera_position = [(1831100., 5642142., 8168.),
                     (1820841., 5648745., 1104.),
                     (-0.435, 0.248, 0.865)]
p.show()
topo map

Total running time of the script: ( 0 minutes 7.064 seconds)

Gallery generated by Sphinx-Gallery