Demo: Build city and meshes¶

This demo can be found in the demos directory. To run the demo, type:

$ python build_city_and_meshes.py

Purpose¶

This demo illustrates how to build city models and meshes from raw data.

Step-by-step¶

To use DTCC Platform, we first need to import the package in Python. We also choose to import Path from the pathlib package, which is useful for handling file paths.

from dtcc import *
from pathlib import Path

We next need to set paths to the data that will be used. In the current demo, we need to set a path to the buildings shapefile and also to the directory containing the point cloud data.

data_directory = Path("data/helsingborg-residential-2022")
buildings_path = data_directory / "footprints.shp"
pointcloud_path = data_directory

The building process can be controlled via a number of parameters. For the current demo, we will use the default parameters. For illustration purposes, we also choose to set the auto_domain parameter to True. This will automatically calculate the domain bounds from the buildings shapefile and the point cloud data (by taking the intersection of the bounds of the two data).

p = parameters.default()
p["auto_domain"] = True

We next calculate the bounds from the buildings shapefile and the point cloud by supplying the data paths and the parameters. The returned origin object is the origin of the domain and the bounds object is the bounds of the domain.

origin, bounds = calculate_bounds(buildings_path, pointcloud_path, p)

We can now load the city model and the point cloud data. The load_city function takes the buildings shapefile path and the bounds as input. The load_pointcloud function takes the point cloud data directory path, and the bounds as input.

city = load_city(buildings_path, bounds=bounds)
pointcloud = load_pointcloud(pointcloud_path, bounds=bounds)

The city model can now be built from the loaded data. The build_city function takes the city model, the point cloud data, the bounds, and the parameters as input.

city = build_city(city, pointcloud, bounds, p)

To build the meshes for the generated city model, we may call the two functions build_mesh and build_volume_mesh. The build_mesh function returns one mesh for the ground and a separate mesh for the buildings. The build_volume_mesh function returns one tetrahedral volume mesh for the city volume (the empty space between the ground, the buildings and an enclosing bounding box) and a separate mesh for the boundary of the volume mesh.

ground_mesh, building_mesh = build_mesh(city, p)
volume_mesh, volume_mesh_boundary = build_volume_mesh(city, p)

To save the city model and the meshes to file, we may call the save method on each object. The .save function takes the data path as input. We here choose to save the city model and the meshes to Protobuf format.

city.save(data_directory / "city.pb")
ground_mesh.save(data_directory / "ground_mesh.pb")
building_mesh.save(data_directory / "building_mesh.pb")
volume_mesh.save(data_directory / "volume_mesh.pb")
volume_mesh_boundary.save(data_directory / "volume_mesh_boundary.pb")

We may also view the generated data in the 3D viewer. To view the data, we call the .view method on each object.

city.view()
pointcloud.view()
volume_mesh_boundary.view()

Complete code¶

The complete code for the demo is shown below.