TwinCore
  1. Base data
  2. Digital twin
  3. Construction control
  4. Construction monitoring
  5. Sales

Digital twin

The unified data becomes a 3D digital twin of the city, used for analysis and risk modelling.

Level of detail (CityGML LOD)

Sample data

LOD3

Façade detail: windows, balconies, entrance, doors.

Risk modelling

Sample data

On the terrain model, see where the river spreads as it rises: first the channel, then the floodplain, finally the street and buildings.

Above normal river level
+2.4 m
Land under water
2.4 ha
Buildings reached
2
Situation
Floodplain under water

Illustrative — real runs use the terrain model and the 3D city model.

GeoAI — object detection

Sample data

A deep-learning model finds and counts buildings, cars, trees and other objects in aerial imagery.

65
Objects
91%
Avg confidence
1.6 s
Time
Orthophoto · 0.2 km²
0.960.980.930.910.920.970.920.930.970.930.960.980.990.950.970.94
Click a class to show or hide it; hover to highlight it. The numbers are confidence scores.

What it covers

  1. 1Digital twin modelBuildings, roads, green space and utility networks, modelled with their classes and attributesLOD1–LOD2 massingLOD3 façadeLOD4 interior4D comparisonIoT / API
  2. 2GeoAI analysisDeep learning finds and counts objects in imagery and builds distribution layersObject detectionCountingPollution sources
  3. 3Risk modellingSimulations on the terrain and 3D city model, used to compare planning optionsFlood spreadWind and air pollutionTraffic load and congestion
  4. 4Suitability assessmentThe master plan checked against building codes, shown as thematic maps and figuresCodes and rulesThematic mapsIndicators