Demo: Build City¶

This demo illustrates how to build a complete city model by combining point cloud data and building footprints to generate terrain rasters, meshes, and LOD1 buildings.

To run the demo, type:

$ python build_city.py

Purpose¶

This demo demonstrates the end-to-end workflow of downloading data, processing it, and constructing a city model. Specifically, it covers: - Downloading point cloud data and building footprints. - Removing outliers from the point cloud. - Building a terrain raster and mesh from the processed data. - Extracting roof points and computing building heights. - Generating LOD1 (Level of Detail 1) buildings. - Creating a city model by assembling the buildings and terrain geometries. - Visualizing the final city model using the 3D viewer.

Step-by-step¶

Define Bounds: Specify the spatial bounds for a residential area in Helsingborg by setting a height/width value (here, 2000.0 units).
```
h = 2000.0
bounds = dtcc.Bounds(319891, 6399790, 319891 + h, 6399790 + h)
```

Download Data: Retrieve the point cloud and building footprints within the defined bounds.

pointcloud = dtcc.download_pointcloud(bounds=bounds)
buildings = dtcc.download_footprints(bounds=bounds)

Process the Point Cloud: Remove global outliers from the point cloud to improve data quality.
```
pointcloud = pointcloud.remove_global_outliers(3.0)
```

Build Terrain Raster and Mesh: Construct a terrain raster and a corresponding mesh from the processed point cloud. These represent the underlying terrain of the city model.

raster = dtcc.builder.build_terrain_raster(pointcloud, cell_size=5,
                                           ground_only=True)
mesh = dtcc.builder.build_terrain_mesh(raster)

Process Building Footprints: Extract roof points from the building footprints and compute building heights using the terrain raster.

buildings = dtcc.extract_roof_points(buildings, pointcloud)
buildings = dtcc.compute_building_heights(buildings, raster, overwrite=True)

Build LOD1 Buildings: Generate detailed (LOD1) building representations using the computed building heights and terrain data.

buildings = dtcc.builder.build_lod1_buildings(
    buildings, default_ground_height=raster.min,
    always_use_default_ground=True
)

Create City Model: Assemble the city model by creating a city object and adding the processed buildings and terrain geometries.
```
city = dtcc.City()
city.add_buildings(buildings)
city.add_terrain(raster)
city.add_terrain(mesh)
```
Visualize the City: Display the constructed city model using the 3D viewer.
```
city.view()
```

Complete Code¶

Below is the complete code for this demo:

import dtcc

# Define bounds (a residential area in Helsingborg)
h = 2000.0
bounds = dtcc.Bounds(319891, 6399790, 319891 + h, 6399790 + h)

# Download pointcloud and building footprints
pointcloud = dtcc.download_pointcloud(bounds=bounds)
buildings = dtcc.download_footprints(bounds=bounds)

# Remove global outliers
pointcloud = pointcloud.remove_global_outliers(3.0)

# Build terrain raster and mesh
raster = dtcc.builder.build_terrain_raster(pointcloud, cell_size=5, ground_only=True)
mesh = dtcc.builder.build_terrain_mesh(raster)

# Extract roof points and compute building heights
buildings = dtcc.extract_roof_points(buildings, pointcloud)
buildings = dtcc.compute_building_heights(buildings, raster, overwrite=True)

# Build LOD1 buildings
buildings = dtcc.builder.build_lod1_buildings(
    buildings, default_ground_height=raster.min, always_use_default_ground=True
)

# Create city and add buildings and geometries
city = dtcc.City()
city.add_buildings(buildings)
city.add_terrain(raster)
city.add_terrain(mesh)

# View city
city.view()