GIS Pipeline

The GIS pipeline turns geospatial source data into 3D scene assets stored in S3, ready for AODT simulations and the viewer.

This quickstart shows how to access a pregenerated scene, as well as create two different maps: one from OpenStreetMap (OSM) and one from CityGML files. For deeper background, concepts, tuning, and troubleshooting, see Scene Generation.

Prerequisites

Before running these examples, confirm:

1. The worker stack is running, including the GIS worker container and Temporal services.
2. The client is installed and dt_client and _config are importable.
3. An S3-compatible bucket is reachable. The default worker stack ships with MinIO at http://<worker-host>:9002 and credentials minioadmin / minioadmin.

A loaded simulation scenario (initiated with start(yaml_content)) is not required for prepare_map.

Use the generated scene

Reference the output key in your simulation YAML under gis.scene.scene_url:

1
gis:
2
  scene:
3
    scene_url: demo_gis/tokyo
4
  vegetation:
5
    active: false
6
    geojson: []

The same key works as the first argument to SimConfig() when you build the YAML in code:

1
from _config import SimConfig, SimMode
2
3
config = SimConfig("demo_gis/tokyo", SimMode.EM, "examples/example_client_assets.yml")

See Configuring Sim YAML for the full sim YAML reference.

The viewer reads Cesium 3D Tiles from <scene_url>/viz/tiles/ and quantized-mesh terrain from <scene_url>/viz/terrain/. See Viewer Installation for connection settings.

Generate a map from OSM

The simplest way to create a map is via OSM. With this method, there is no need to source GIS data yourself. Instead, source data is automatically retrieved from OSM and other publicly available sources. Only a bounding box of your area of interest is needed.

From the client/ directory, with the worker reachable:

$
python examples/example_prepare_map.py \
>
  --s3_endpoint http://<worker-host>:9002

That run writes a Seattle bounding-box scene to s3://aerial-data/demo_gis/test_osm.usd/ using MinIO defaults.

Pick your own region with bounding-box flags and an output key:

$
python examples/example_prepare_map.py \
>
  --s3_endpoint http://<worker-host>:9002 \
>
  --min_lon 139.74 --min_lat 35.66 \
>
  --max_lon 139.75 --max_lat 35.67 \
>
  --output_folder_key demo_gis/tokyo

Expected output:

Connecting to Digital Twin server at localhost:50051
Preparing OSM map -> s3://aerial-data/demo_gis/tokyo
(streaming heartbeats will appear below while the workflow runs)
...
Map ready: s3://aerial-data/demo_gis/tokyo

Once successfully run, map files will be generated in the target endpoint. These files can be browsed under <output_folder_key>/sim/ and <output_folder_key>/viz/. For more details on what these files are, visit the Scene Generation output layout.

Generate a map from CityGML

CityGML files are a supported data source. As an example data source for Japan, consider PLATEAU.

Point the example at server-accessible CityGML files and pass the input EPSG code:

$
python examples/example_prepare_map.py \
>
  --task gml \
>
  --s3_endpoint http://<worker-host>:9002 \
>
  --input_files /data/buildings.gml /data/terrain.gml \
>
  --epsg_in 6697 \
>
  --output_folder_key demo_gis/my_gml_scene

GML jobs require non-empty --input_files and a valid --epsg_in for the source coordinate system. The paths must be reachable from the GIS worker container, not just the client machine.

Next steps

• Run an EM simulation or RAN simulation against your new scene.
• Read Scene Generation for output layout, tuning, the task field reference, and common errors.
• Check Limitations.
• See the DigitalTwinClient API for the underlying call signature.