DTM Processing & Ground Enrichment

Comprehensive guide to Digital Terrain Model (DTM) processing for LiDAR point cloud enrichment and terrain analysis.

Overview

The IGN LiDAR HD library uses RGE ALTI (French national elevation reference) to enhance LiDAR point clouds by:

Ground Point Enrichment - Adding synthetic ground points in sparse areas
Height Computation - Calculating accurate height-above-ground values
Variable Object Filtering - Identifying temporary objects using terrain reference
Automatic Fallback - Seamless switching between LiDAR HD MNT and RGE ALTI sources

Ground Point Enrichment
DTM Fallback Strategy
Variable Object Filtering
Configuration
API Reference
Best Practices
Troubleshooting

Ground Point Enrichment

What is Ground Enrichment?

Ground enrichment adds synthetic ground points from DTM data to fill gaps in LiDAR coverage, particularly:

Under buildings - Where roofs block ground returns
Under dense vegetation - Where tree canopies absorb laser pulses
Coverage gaps - From flight path overlaps or sensor dropouts

Three Enrichment Modes

1. Building Mode

Adds ground points under building footprints using BD TOPO vector data.

Configuration:

dtm_enrichment:
  enable_building_enrichment: true
  spacing_buildings: 1.0 # 1m grid spacing
  buffer_margin: 0.5 # Shrink footprints 0.5m inward

Output:

~1M synthetic points per km² of buildings
Complete ground coverage under structures
Smooth terrain transitions at building edges

2. Vegetation Mode

Adds ground points under tall vegetation (>2m height).

Configuration:

dtm_enrichment:
  enable_vegetation_enrichment: true
  spacing_vegetation: 2.0 # 2m grid spacing
  vegetation_height_threshold: 2.0 # Only vegetation >2m tall

Output:

~250K synthetic points per km² of forest
Ground surface under tree canopy
Preserves understory structure

Prerequisites

Requires height_above_ground to be computed first!

3. Gap Filling Mode

Detects and fills areas with abnormally low point density.

Configuration:

dtm_enrichment:
  enable_gap_enrichment: true
  spacing_gaps: 3.0 # 3m grid spacing
  gap_density_threshold: 0.1 # <0.1 pts/m² = gap
  gap_analysis_cell_size: 10.0 # Analyze in 10m cells

Output:

Variable points depending on gap size
Minimum viable coverage in sparse areas

Quick Start Example

from ign_lidar.io.dtm_ground_enrichment import enrich_laz_with_dtm_ground
import numpy as np

# Your data
points = np.load("tile_points.npy")  # Shape: (N, 3)
labels = np.load("tile_labels.npy")   # Shape: (N,)
bbox = (650000, 6860000, 651000, 6861000)  # EPSG:2154

# Enrich!
enriched_points, enriched_labels, stats = enrich_laz_with_dtm_ground(
    points=points,
    labels=labels,
    bbox=bbox,
    spacing_buildings=1.0,
    spacing_vegetation=2.0,
    spacing_gaps=3.0
)

# Results
print(f"Added {stats.total_synthetic:,} synthetic ground points")
print(f"  Under buildings: {stats.synthetic_under_buildings:,}")
print(f"  Under vegetation: {stats.synthetic_under_vegetation:,}")
print(f"  In gaps: {stats.synthetic_in_gaps:,}")

Performance

Operation	Time (per km²)	Memory	Points Added
Building mode	~2-5 sec	~50 MB	~500K-1M
Vegetation mode	~1-3 sec	~30 MB	~200K-500K
Gap mode	~3-7 sec	~40 MB	~50K-300K
Combined	~6-15 sec	~120 MB	~800K-1.8M

DTM Fallback Strategy

Why Fallback is Needed

When fetching DTM from IGN Géoplateforme, the system implements automatic fallback to maximize data availability:

Primary: LiDAR HD MNT (1m resolution, best quality)
Fallback: RGE ALTI (1-5m resolution, broader coverage)

Common issues:

Service interruptions (502 Bad Gateway errors)
Coverage gaps (LiDAR HD MNT not available everywhere)
Maintenance windows
High server load during peak usage

How Fallback Works

Log Messages

Successful Primary Fetch

[INFO] Fetching DTM from IGN WMS (LiDAR HD MNT): (635000.0, 6856000.0, 636000.0, 6857000.0)
[INFO] ✅ Successfully fetched DTM using LiDAR HD MNT
[INFO] ✅ Added 1,234,567 synthetic ground points from DTM

Successful Fallback

[INFO] Fetching DTM from IGN WMS (LiDAR HD MNT): ...
[WARNING] WMS fetch failed for LiDAR HD MNT: 502 Server Error: Bad Gateway
[INFO] Fetching DTM from IGN WMS (RGE ALTI (fallback)): ...
[INFO] ✅ Successfully fetched DTM using RGE ALTI (fallback)
[INFO] ✅ Added 1,234,567 synthetic ground points from DTM

Complete Failure

[WARNING] WMS fetch failed for LiDAR HD MNT: 502 Server Error
[WARNING] WMS fetch failed for RGE ALTI (fallback): 502 Server Error
[ERROR] Failed to fetch DTM from all WMS layers
[WARNING] Skipping ground augmentation - using existing LiDAR ground points only
[INFO] 💡 Tip: Check your internet connection or consider pre-downloading DTM tiles

Quality Comparison

Aspect	LiDAR HD MNT	RGE ALTI
Resolution	1m (always)	1-5m (varies)
Source	LiDAR point clouds	Mixed sources
Quality	Highest (±0.15m)	Good (±0.30m)
Coverage	Limited (newer areas)	Complete (all France)
Consistency	Perfect with input	Good
Use Case	Best for LiDAR	Fallback/broad

Variable Object Filtering

Principle

DTM provides a stable terrain reference to calculate precise height-above-ground:

height_above_ground = point_Z - DTM_elevation_at_XY

This enables identification and filtering of temporary/variable objects.

Objects to Filter

1. Vehicles (Cars, Trucks, Buses)

Characteristics:

Height: 1.4-1.8m (cars), 2.5-4.0m (trucks/buses)
Location: Roads, parking lots, service areas
Signature: Horizontal flat surfaces + vertical walls

Filtering Strategy:

# On roads (class 11)
vehicle_mask = (classification == 11) & (height_above_ground > 0.8)
classification[vehicle_mask] = 1  # Reclassify as "unassigned"

# In parking lots (class 40)
vehicle_mask = (
    (classification == 40) &
    (height_above_ground > 0.5) &
    (height_above_ground < 3.0)
)
classification[vehicle_mask] = 1

Recommended thresholds:

Roads: 0.8m < height < 3.0m → probably vehicle
Parking: 0.5m < height < 4.0m → probably vehicle
Railways: 1.5m < height < 5.0m → probably train/wagon

2. Walls and Fences

Characteristics:

Height: 0.5-2.5m
Geometry: Linear vertical structures
Verticality: > 0.8

Filtering Strategy:

# Detect walls (combine height + verticality)
wall_mask = (
    (height_above_ground > 0.5) &
    (height_above_ground < 2.5) &
    (verticality > 0.8) &
    (planarity > 0.7)
)

# Option 1: Create "wall" class (61)
classification[wall_mask] = 61

# Option 2: Reclassify by context
# - Near building → part of building (6)
# - Between parcels → fence/boundary

3. Street Furniture

Characteristics:

Height: 0.5-4.0m
Size: Small clusters < 2m²
Location: Sidewalks, squares, parks

Examples: Benches, signs, poles, bollards, public lighting

Filtering Strategy:

# Detect small elevated objects
furniture_mask = (
    (height_above_ground > 0.5) &
    (height_above_ground < 4.0) &
    (cluster_size < 2.0) &  # Small objects
    (classification == 1)    # Unclassified
)

# Create custom class or merge with existing
classification[furniture_mask] = 14  # Bridge/Elevated

Configuration

Complete Example

# DTM Ground Enrichment Settings
dtm_enrichment:
  enabled: true

  # Enable/disable modes
  enable_building_enrichment: true
  enable_vegetation_enrichment: true
  enable_gap_enrichment: false # Disabled by default

  # Spacing parameters (meters)
  spacing_buildings: 1.0 # Dense grid under buildings
  spacing_vegetation: 2.0 # Medium grid under vegetation
  spacing_gaps: 3.0 # Sparse grid in gaps

  # Filtering parameters
  min_distance: 0.5 # Min distance to existing ground (m)
  buffer_margin: 0.5 # Shrink building footprints (m)

  # Vegetation mode settings
  vegetation_height_threshold: 2.0 # Only >2m vegetation

  # Gap detection settings
  gap_density_threshold: 0.1 # <0.1 pts/m² = gap
  gap_analysis_cell_size: 10.0 # Analyze in 10m cells

# DTM Data Source Configuration
data_sources:
  rge_alti:
    enabled: true
    use_wcs: true # Enable WMS downloads with fallback
    resolution: 1.0
    cache_enabled: true # Strongly recommended
    cache_dir: null # Auto: {input_dir}/cache/rge_alti
    cache_ttl_days: 90 # DTM rarely changes
    local_dtm_dir: null # Optional: path to pre-downloaded tiles

# Ground Truth Data (for building footprints)
ground_truth:
  bd_topo:
    enabled: true
    path: ./data/ground_truth/BDTOPO/
    features:
      buildings:
        enabled: true
        file: BATIMENT.shp

Minimal Configuration

dtm_enrichment:
  enable_building_enrichment: true
  enable_vegetation_enrichment: false
  enable_gap_enrichment: false
  spacing_buildings: 1.5

API Reference

DTMGroundEnricher

Main enrichment engine.

class DTMGroundEnricher:
    def __init__(
        self,
        dtm_fetcher: Optional[RGEALTIFetcher] = None,
        spacing_buildings: float = 1.0,
        spacing_vegetation: float = 2.0,
        spacing_gaps: float = 3.0,
        min_distance: float = 0.5,
        buffer_margin: float = 0.5,
        vegetation_height_threshold: float = 2.0,
        gap_density_threshold: float = 0.1,
        gap_analysis_cell_size: float = 10.0,
        enable_building_enrichment: bool = True,
        enable_vegetation_enrichment: bool = True,
        enable_gap_enrichment: bool = True
    )

enrich_point_cloud()

Main enrichment method.

def enrich_point_cloud(
    self,
    points: np.ndarray,          # Point cloud coordinates (N × 3)
    labels: np.ndarray,          # ASPRS classification labels (N,)
    bbox: Tuple[float, ...],     # (xmin, ymin, xmax, ymax)
    ground_truth_features: Optional[Dict] = None,  # {'buildings': GeoDataFrame}
    height_above_ground: Optional[np.ndarray] = None  # Heights (N,)
) -> Tuple[np.ndarray, np.ndarray, EnrichmentStats]

Returns:

enriched_points: Merged point coordinates (M × 3)
enriched_labels: Merged classification labels (M,)
stats: EnrichmentStats with detailed metrics

enrich_laz_with_dtm_ground()

Convenience wrapper function.

def enrich_laz_with_dtm_ground(
    points: np.ndarray,
    labels: np.ndarray,
    bbox: Tuple[float, float, float, float],
    spacing_buildings: float = 1.0,
    spacing_vegetation: float = 2.0,
    spacing_gaps: float = 3.0,
    **kwargs
) -> Tuple[np.ndarray, np.ndarray, EnrichmentStats]

EnrichmentStats

Results dataclass.

@dataclass
class EnrichmentStats:
    original_points: int              # Original point count
    synthetic_under_buildings: int    # Points added under buildings
    synthetic_under_vegetation: int   # Points added under vegetation
    synthetic_in_gaps: int            # Points added in gaps
    total_synthetic: int              # Total synthetic points
    duplicate_filtered: int           # Duplicates removed
    final_points: int                 # Final enriched point count

Best Practices

1. Enable Caching

Always enable DTM caching to reduce network requests:

data_sources:
  rge_alti:
    cache_enabled: true
    cache_dir: /data/cache/rge_alti # Persistent location
    cache_ttl_days: 90

Benefits:

5-10× speedup on repeated runs
Protection against service interruptions
Reduced server load
Offline processing capability

2. Compute HAG Before Enrichment

# ❌ BAD: Vegetation mode without HAG
enriched = enrich_laz_with_dtm_ground(
    points, labels, bbox,
    spacing_vegetation=2.0
    # Missing height_above_ground!
)

# ✅ GOOD: Compute HAG first
hag = compute_height_above_ground(points, labels)
enriched = enrich_laz_with_dtm_ground(
    points, labels, bbox,
    height_above_ground=hag,
    spacing_vegetation=2.0
)

3. Tune Spacing for Your Use Case

Use Case	Buildings	Vegetation	Gaps
High accuracy DTM	0.5m	1.0m	2.0m
Balanced (default)	1.0m	2.0m	3.0m
Performance priority	2.0m	3.0m	5.0m

4. Monitor Fallback Usage

# Count fallback occurrences in logs
grep "fallback" pipeline.log | wc -l

# Show successful primary fetches
grep "Successfully fetched DTM using LiDAR HD MNT" pipeline.log | wc -l

# Show fallback fetches
grep "Successfully fetched DTM using RGE ALTI" pipeline.log | wc -l

5. Pre-download for Large Projects

For large-scale processing or poor connectivity, pre-download DTM tiles:

data_sources:
  rge_alti:
    use_local: true
    local_dtm_dir: "/data/rge_alti_tiles/"

Troubleshooting

Problem: No Synthetic Points Added

Symptoms:

stats.total_synthetic == 0

Possible Causes:

All enrichment modes disabled
No building footprints provided
Missing height_above_ground for vegetation mode
DTM data unavailable

Solutions:

# Check modes are enabled
assert enricher.enable_building_enrichment or \
       enricher.enable_vegetation_enrichment or \
       enricher.enable_gap_enrichment

# Provide buildings for building mode
enriched = enrich_point_cloud(
    ground_truth_features={'buildings': buildings_gdf}
)

# Compute HAG for vegetation mode
hag = compute_height_above_ground(points, labels)
enriched = enrich_point_cloud(height_above_ground=hag)

Problem: WMS Fetch Failures

Symptoms:

[ERROR] Failed to fetch DTM from all WMS layers

Solutions:

Check internet: curl https://data.geopf.fr/wms-r/wms
Enable caching to use cached data
Wait and retry (services usually recover quickly)
Pre-download DTM tiles for offline use

Problem: Performance Too Slow

Symptoms:

Enrichment takes >30 seconds per km²

Solutions:

Reduce spacing (fewer points):

spacing_buildings=2.0  # Was 1.0
spacing_vegetation=4.0  # Was 2.0

Disable gap mode (most expensive):
```
enable_gap_enrichment=False
```
Use local DTM (avoid downloads):
```
local_dtm_dir="/data/rge_alti/"
```

Problem: Wrong Elevations

Symptoms:

Synthetic points above/below expected terrain

Debug:

# Check DTM elevation range
dtm = fetcher.get_dtm_for_bbox(bbox)
print(f"DTM range: {dtm.min():.1f} - {dtm.max():.1f}m")

# Compare with point cloud
print(f"Point Z range: {points[:,2].min():.1f} - {points[:,2].max():.1f}m")

# Sample comparison
test_xy = points[:10, :2]
test_z_dtm = fetcher.sample_elevation_at_points(test_xy, bbox)
test_z_actual = points[:10, 2]
print(f"Mean Z difference: {np.abs(test_z_dtm - test_z_actual).mean():.2f}m")

RGE ALTI Integration - Full RGE ALTI setup guide
Ground Truth Classification - Using ground truth data
Configuration Guide - Complete configuration reference
Height Computation - HAG calculation

References

IGN RGE ALTI 1m - Official documentation
BD TOPO V3 - Building footprint source
ASPRS LAS 1.4 - Classification standard

Last updated: October 21, 2025

Overview​

Table of Contents​

Ground Point Enrichment​

What is Ground Enrichment?​

Three Enrichment Modes​

1. Building Mode​

2. Vegetation Mode​

3. Gap Filling Mode​

Quick Start Example​

Performance​

DTM Fallback Strategy​

Why Fallback is Needed​

How Fallback Works​

Log Messages​

Successful Primary Fetch​

Successful Fallback​

Complete Failure​

Quality Comparison​

Variable Object Filtering​

Principle​

Objects to Filter​

1. Vehicles (Cars, Trucks, Buses)​

2. Walls and Fences​

3. Street Furniture​

Configuration​

Complete Example​

Minimal Configuration​

API Reference​

DTMGroundEnricher​

enrich_point_cloud()​

enrich_laz_with_dtm_ground()​

EnrichmentStats​

Best Practices​

1. Enable Caching​

2. Compute HAG Before Enrichment​

3. Tune Spacing for Your Use Case​

4. Monitor Fallback Usage​

5. Pre-download for Large Projects​

Troubleshooting​

Problem: No Synthetic Points Added​

Problem: WMS Fetch Failures​

Problem: Performance Too Slow​

Problem: Wrong Elevations​

Related Documentation​

References​

Overview

Table of Contents

Ground Point Enrichment

What is Ground Enrichment?

Three Enrichment Modes

1. Building Mode

2. Vegetation Mode

3. Gap Filling Mode

Quick Start Example

Performance

DTM Fallback Strategy

Why Fallback is Needed

How Fallback Works

Log Messages

Successful Primary Fetch

Successful Fallback

Complete Failure

Quality Comparison

Variable Object Filtering

Principle

Objects to Filter

1. Vehicles (Cars, Trucks, Buses)

2. Walls and Fences

3. Street Furniture

Configuration

Complete Example

Minimal Configuration

API Reference

DTMGroundEnricher

enrich_point_cloud()

enrich_laz_with_dtm_ground()

EnrichmentStats

Best Practices

1. Enable Caching

2. Compute HAG Before Enrichment

3. Tune Spacing for Your Use Case

4. Monitor Fallback Usage

5. Pre-download for Large Projects

Troubleshooting

Problem: No Synthetic Points Added

Problem: WMS Fetch Failures

Problem: Performance Too Slow

Problem: Wrong Elevations

Related Documentation

References