Data compilation
Five distinct data streams are integrated into the BEAM Digital Twin — from physical sensor readings to 3D spatial models, geospatial layers, and machine learning outputs. Each stream is processed differently before being served to the platform.
The DT's 3D environment combines two complementary building model sets — one providing high geometric detail, the other reflecting more recent construction. Both cover NUS Kent Ridge Campus including UTown, excluding Kent Vale.
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| Attribute | 2019–2020 model | 2023 model |
|---|---|---|
| Source | Virtual Campus project | GPS Lands LiDAR scan |
| File format | .STL | .OBJ |
| File size | 431 MB | ~5 MB |
| Level of detail | LOD 3.2 — high detail | LOD 2.1 — simplified |
| Coverage | Kent Ridge + UTown | Kent Ridge + UTown |
| Limitations | Missing newer buildings (Techno Edge, E7, COM3, S9) | Missing latest buildings (Valour House, retrofitted YIH) |
Ref: BEAM Phase 1 Report §4.3.1, Table 4
A comprehensive vegetation model built from three separate data sources — each capturing a different layer of the campus's green cover, from individual roadside trees down to forest canopies and ground-level shrubs.
Tree inventory shapefile from UCI — containing species, height, canopy spread, and trunk girth — imported into Grasshopper/Rhino via the Urbano plugin. Each tree point is matched to a species in the BIM vegetation library and generated as a full 3D geometry.
Roadside treesCampus-wide laser-scan point cloud clipped beneath tree canopies to isolate shrub and turf layers. Outlines are extracted and species assigned parametrically — capturing vegetation that satellite imagery or manual surveys cannot detect beneath the canopy.
Shrubs + turfMaxar satellite DEM at 0.5 m resolution used to model dense forest areas and groves. Greenery blocks follow forest boundary and height profiles; trees inside are generated randomly but matched to surrounding species for ecological accuracy.
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All Rhino-generated models — trees, shrubs, turf, buildings, and roads — are imported into ArcGIS Pro maintaining accurate geospatial coordinates. The 3D and GIS models remain synchronised, enabling diverse spatial analyses and correlations with weather station data.
The 3D greenery model can be converted to ENVI-Met for microclimate simulations. Each tree is represented as pixel cubes incorporating Leaf Area Index (LAI), albedo, emissivity, and transmittance. Terrain data ensures buildings and greenery follow actual ground contours.
Ref: Lu et al. (2025) Journal of Digital Landscape Architecture · BEAM Phase 1 Report §4.4
Real-time microclimate data from 49 instruments across NUS Kent Ridge — weather stations, IR thermal cameras, and meteorological towers — flowing hourly into the DT via AWS. For full sensor specifications and deployment details, see the Sensor Network page.
Raw 1-minute sensor readings are compiled and aggregated to hourly values by AWS Lambda functions. The DT frontend receives clean, formatted JSON via API Gateway — ready to render directly in charts and heatmaps without further processing client-side.
Current view: past 30 days of data, updated every hour.
Historical archive: monthly compiled files since late 2023.
Weather stations (40): air temperature, humidity, wind, solar irradiance, rainfall — time-series per station.
IR cameras (6): thermal images (464×348 px, every 30 min) and visible reference images (every 60 min) stored as PNG in S3.
Met towers (3): vertical profiles at 3, 6, 9, 12 m heights — temperature, humidity, wind, surface temperature at MD1.
Nine independently toggleable geospatial layers form the 2D environment of the DT — sourced from the greenery database, 2019–2020 Virtual Campus GIS, and satellite-derived data. Each layer is served as a GeoJSON file from S3 and rendered by deck.gl.
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| Layer | Geometry type | Source |
|---|---|---|
| Buildings | Polygon | 3D model (derived) |
| Big trees · Small trees | Point | Greenery database |
| Tree canopy | Polygon | Greenery database |
| Shrubs | Polygon | Greenery database |
| Turf | Polygon | Greenery database |
| Forest | Polygon | Virtual Campus 2019–2020 |
| Roads · Pavement · Sports fields | Polygon | Virtual Campus 2019–2020 |
| Administrative zones (1, 2, 3, 4, 7) | Polygon | Virtual Campus 2019–2020 |
| NUS boundary | Polygon | Virtual Campus 2019–2020 |
Ref: BEAM Phase 1 Report §4.3.2, Table 5