In telecom, 2D geodata classifies the land surface by type (urban, forest, water) without height information. 2.5D geodata adds a single height value per surface pixel, typically derived from a Digital Surface Model, enabling approximate clutter height modeling. 3D geodata provides full geometric objects: building footprint polygons with explicit height attributes, individual tree heights, and terrain elevation as separate layers, enabling deterministic propagation modeling.
Radio signals interact with the physical world in three dimensions. A signal traveling from a base station to a receiver may pass over buildings, diffract around rooftop edges, reflect off facades, and lose energy as it passes through trees. Each of these interactions is a 3D geometric event.
The degree to which your geodata captures 3D geometry determines how accurately a propagation model can simulate these interactions, and therefore how reliable your coverage predictions are.
For 4G at sub-2 GHz, simplified representations (2D or 2.5D) were often adequate because signal behavior was more forgiving. For 5G at 3.5 GHz and above, the interactions become more precise and the geometric accuracy required increases substantially.
2D geodata assigns a single attribute, typically a land-use or land-cover classification, to each location on the ground. In telecom, this is usually called clutter data.
A 2D clutter map is a raster grid where each pixel is assigned a category code:
Each category maps to a propagation attenuation coefficient in the planning tool's propagation model. "Dense Urban" receives a high attenuation coefficient; "Open" receives a low one.
2.5D geodata assigns a height value to each surface pixel in addition to (or instead of) its land-use classification. The "2.5" label reflects that this is not truly three-dimensional. It is a two-dimensional surface with a single height value per pixel, not a volumetric 3D object.
1. Digital Surface Model (DSM)
A DSM captures the height of the topmost surface at each pixel. Buildings, tree canopies, terrain, and all other objects are merged into a single elevation value. The DTM (bare earth) is subtracted from the DSM to produce a normalized DSM (nDSM) that shows height above ground.
DSMs are produced from:
2. Clutter Height Data
Clutter height data combines a land-use classification with a representative height per pixel derived from the DSM. This tells the propagation model both what type of environment is present (for attenuation coefficient selection) and how tall the features in that pixel are (for diffraction calculations).
This is a meaningful improvement over pure 2D data because it allows the model to compute approximate diffraction loss at the average height of the clutter type.
Even with height values, 2.5D data cannot:
3D geodata represents objects in the environment as distinct geometric entities with explicit three-dimensional geometry. In telecom, the key 3D layers are:
Building vectors are polygon footprints with associated height attributes. They come in two primary levels of detail:
LOD1 (Level of Detail 1):
LOD2 (Level of Detail 2):
Unlike DSM data that averages canopy height per pixel, 3D vegetation data provides:
This separation of trunk and canopy heights is critical for 5G propagation because at 3.5 GHz and 26 GHz, the specific geometry of the canopy relative to the signal path, rather than the presence of "forest" as a class, determines the attenuation value.
A study using LuxCarta data in Barcelona found that modeling 3.6 GHz propagation with individual 3D tree data versus no tree data produced a 12.8% improvement in prediction accuracy. At 26 GHz, a single tree in the LoS path can cause 35.3 dB of attenuation, equivalent to total blockage in most link budget scenarios.
The DTM is the 3D representation of the bare earth surface, with ground elevation and all vegetation and buildings removed. While it is technically a 2.5D surface (single height per point), it is treated as a foundational 3D layer because it forms the geometric base from which building heights and signal paths are referenced.
| Capability | 2D Clutter | 2.5D Clutter Height | 3D Building Vectors |
|---|---|---|---|
| Statistical attenuation modeling | Yes | Yes | Yes |
| Approximate diffraction height | No | Yes (pixel average) | Yes (building-specific) |
| Deterministic LoS/NLoS | No | No | Yes |
| Individual building diffraction | No | No | Yes |
| Street canyon modeling | No | No | Yes |
| Per-building FWA qualification | No | No | Yes |
| Individual tree attenuation | No | Partial (if veg. class has height) | Yes (with 3D veg. layer) |
| Compatible with ray-tracing | No | Partial | Yes |
| Compatible with empirical models | Yes | Yes | Yes |
Resolution and dimensionality are independent but related. A 3D building dataset at 50 m horizontal resolution (very large buildings only) is less useful than a 3D building dataset at 0.5 m horizontal resolution (all buildings including small structures).
Practical resolution targets for each data type:
2D Clutter:
2.5D DSM / Clutter Height:
3D Building Vectors:
| Scenario | Recommended Data Type |
|---|---|
| Sub-1 GHz rural macro planning | 2D clutter + DTM |
| Sub-1 GHz urban macro | 2.5D clutter height + DTM |
| 3.5 GHz suburban macro | 2.5D clutter height + DTM |
| 3.5 GHz urban macro | 3D buildings + DTM + LULC |
| 3.5 GHz small cells | 3D buildings + 3D vegetation + DTM |
| 26 GHz mmWave urban | 3D buildings (LOD1/LOD2) + 3D vegetation + DTM |
| 5G FWA qualification | 3D buildings + 3D vegetation + DTM |
| Private 5G campus network | 3D buildings (LOD2) + detailed DTM |
When in doubt about which data type your propagation model will exploit effectively, test with a small pilot area. Extract results from the same area with 2D clutter only, then add 2.5D height data, then add 3D building vectors. The improvement at each step quantifies the value for your specific propagation model and environment.
LuxCarta delivers all three data dimensions across a single, consistent production pipeline. The same satellite imagery that produces 2D LULC classification also sources the stereo elevation data for DSM/DTM production and the building footprint extraction for 3D vectors. This means all three layers are co-registered, covering the same area with consistent metadata and licensing.
The LULC product covers 18 to 19 land-use classes at 50 cm resolution from sub-meter imagery, one of the most detailed classification schemes available globally for telecom applications. The 3D building product achieves 93%+ capture rate with 87.67% precision and 88.44% recall, and is available in both LOD1 and LOD2. LuxCarta's novel 3D mesh building extraction technique (presented at SPIE 2023) enables LOD2 production at 4x the productivity of manual methods, making high-detail 3D data accessible for large urban footprints without the traditional cost premium.
All products are delivered in formats directly compatible with Forsk Atoll, InfoVista Planet (including the AIM propagation model), TEOCO Asset, and iBwave, the full stack of leading telecom planning tools.
The BrightEarth platform allows operators to extract 2D, 2.5D, and 3D layers for a specific area of interest, enabling direct comparison of planning outputs at each data dimensionality before committing to a full-coverage order.
Yes, and this is a common approach for cost optimization. Urban cores where 5G small cells are being deployed use 3D building data, while surrounding suburban and rural areas continue to use 2D clutter or 2.5D clutter height data. The layers need to be geographically consistent at their boundaries to avoid propagation model discontinuities at the transition zones.
A DSM (Digital Surface Model) captures absolute elevation, that is, ground height plus the height of all features above it. A normalized DSM (nDSM) subtracts the DTM from the DSM to produce a layer showing height above ground only. The nDSM is what planners typically use for building and vegetation height layers, since absolute elevation is already captured in the DTM.
For propagation modeling, 3D building vectors enable deterministic LoS calculations and building-specific diffraction that a DSM cannot. However, a DSM at very high resolution (0.5 m) can approach the utility of LOD1 building vectors in dense urban environments if the DSM is sufficiently artifact-free. Building vectors remain preferable when FWA qualification, street canyon modeling, or indoor planning are required.
For most urban 5G sub-6 GHz planning, the difference between LOD1 and LOD2 is marginal. The flat roof assumption in LOD1 is acceptable. For mmWave planning in European cities with complex roofscapes, LOD2 adds measurable accuracy for rooftop diffraction. LOD2 is also required for digital twin applications and realistic 3D city visualization.
Atoll, Planet, and TEOCO Asset each use their own default clutter classification codes. Data from a single source must often be re-coded to match the target tool's classification scheme. Most commercial geodata vendors, including LuxCarta, can deliver data pre-coded for the target planning tool, avoiding the need for manual reclassification.
LuxCarta provides AI-powered 3D geospatial data solutions for telecom, simulation, and smart city applications worldwide. Learn more at luxcarta.com or explore on-demand extraction at BrightEarth.