Topographic Map

Plot a map from a GeoTIFF on top of a 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

import matplotlib as mpl

Let’s inspect the imagery that we just loaded

import matplotlib.pyplot as plt

mpl.rcParams["figure.dpi"] = 500

plt.imshow(topo_map.to_array())

<matplotlib.image.AxesImage object at 0x7f2f14054730>

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)

Header	Data Arrays
UnstructuredGrid Information N Cells 436733 N Points 222110 X Bounds 1.818e+06, 1.825e+06 Y Bounds 5.645e+06, 5.653e+06 Z Bounds 1.381e+03, 2.787e+03 N Arrays 2	Name Field Type N Comp Min Max scalar1of1 Points float64 1 1.381e+03 2.787e+03 Texture Coordinates Points float32 2 0.000e+00 1.000e+00

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

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

And here is a 3D perspective!

local.plot(texture=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 = [
    (1831100.0, 5642142.0, 8168.0),
    (1820841.0, 5648745.0, 1104.0),
    (-0.435, 0.248, 0.865),
]
p.show()