RGE ALTI Integration Guide

Complete guide to integrating IGN's RGE ALTI® Digital Terrain Model into the LiDAR HD processing pipeline for enhanced ground coverage and accurate height computation.

Overview

RGE ALTI (Référentiel à Grande Échelle pour l'Altimétrie) is France's national high-resolution digital terrain model, providing:

Synthetic ground point generation under vegetation and buildings
Accurate height computation using DTM as ground reference
Improved classification through better height features
Automatic WMS fallback for reliable data access

Key Specifications

Property	Value
Resolution	1 meter (5m fallback available)
Coverage	Mainland France + DOM-TOM
Source	LiDAR and photogrammetric surveys
Format	GeoTIFF raster
Access	IGN Géoplateforme WMS
Accuracy	±0.2m vertical (RMSE)
Update Frequency	Varies by region

Quick Start
Integration Methods
WMS Service & Caching
Configuration
Implementation Details
Migration Guide
Best Practices
Troubleshooting

Quick Start

Basic Setup

# config.yaml
data_sources:
  rge_alti:
    enabled: true
    use_wcs: true # Enables WMS (name kept for compatibility)
    resolution: 1.0 # 1m resolution
    cache_enabled: true # Strongly recommended
    cache_dir: null # Auto: {input_dir}/cache/rge_alti

ground_truth:
  rge_alti:
    augment_ground: true
    augmentation_strategy: "intelligent" # gaps|intelligent|full

Python Usage

from ign_lidar.io.rge_alti_fetcher import RGEALTIFetcher

# Enable WMS with caching (recommended)
fetcher = RGEALTIFetcher(
    cache_dir=".cache/ign_lidar/rge_alti",
    use_wcs=True,  # Actually enables WMS
    resolution=1.0
)

# First call: Downloads from WMS and caches
bbox = (635000.0, 6856000.0, 636000.0, 6857000.0)  # EPSG:2154
dtm_data = fetcher.fetch_dtm_for_bbox(bbox, crs="EPSG:2154")

# Second call: Uses cached file (instant)
dtm_data = fetcher.fetch_dtm_for_bbox(bbox, crs="EPSG:2154")

Integration Methods

RGE ALTI enhances LiDAR processing in three key ways:

1. Ground Point Augmentation

Fills gaps in LiDAR ground coverage by generating synthetic ground points from DTM.

Where gaps occur:

Under buildings - Roofs block ground returns
Dense vegetation - Tree canopies absorb laser pulses
Coverage gaps - Flight path overlaps or sensor dropouts

How it works:

# Generate regular grid of ground points from DTM
synthetic_points = []
for x, y in grid_positions:
    z = dtm.sample_elevation_at_xy(x, y)
    synthetic_points.append([x, y, z])

# Merge with original LiDAR points
enriched_points = concatenate(lidar_points, synthetic_points)

Configuration:

ground_truth:
  rge_alti:
    augment_ground: true
    augmentation_spacing: 2.0 # Grid spacing (meters)
    augmentation_strategy: "intelligent" # See strategies below

Augmentation Strategies:

Strategy	Description	Use Case
`gaps`	Fill only low-density areas	Conservative approach
`intelligent`	Fill under buildings & vegetation	Balanced (recommended)
`full`	Add points everywhere	Maximum coverage

2. Accurate Height Computation

Uses DTM as absolute ground reference instead of local minimum elevation.

Traditional method problems:

❌ Inaccurate under vegetation (uses vegetation base, not true ground)
❌ Unreliable near buildings (uses nearby ground)
❌ Errors in sloped terrain

RGE ALTI solution:

height_above_ground = point_Z - DTM_elevation_at_XY

Benefits:

✅ Accurate heights even under dense vegetation
✅ Correct building heights
✅ Reliable on slopes and complex terrain
✅ 10-30% classification improvement in difficult areas

Configuration:

features:
  height_method: "dtm" # Options: local|dtm|hybrid
  use_rge_alti_for_height: true
  compute_height_above_ground: true

3. Enhanced Classification

Improved height features enable better distinction between classes.

Improvements:

Vegetation - Better low/medium/high separation
Buildings - Accurate building vs low object separation
Roads - Automatic exclusion of roadside trees
Terrain - Better ground vs non-ground classification

Example - Road Classification:

# Traditional (height from local minimum)
road_mask = (height <= 2.0) & (planarity >= 0.7)
# Problem: Includes roadside trees

# With RGE ALTI (height above DTM)
road_mask = (height_above_ground <= 0.5) & (planarity >= 0.7)
# Solution: Only surface points, trees automatically excluded

WMS Service & Caching

IGN Géoplateforme Migration

Service Change

IGN migrated from old Géoservices to Géoplateforme (data.geopf.fr) in October 2025. WCS (Web Coverage Service) is no longer available - use WMS with caching instead.

Data Source Priority

The RGEALTIFetcher attempts to fetch DTM data in this order:

Cache (fastest) - Checks for previously downloaded tiles
Local files - Searches local DTM directory if specified
WMS - Downloads from IGN Géoplateforme and caches result

WMS with Automatic Caching

How it works:

First fetch - Downloads GeoTIFF from WMS, saves to cache
Subsequent fetches - Uses cached file (no network request)
Cache naming - rge_alti_{crs}_{minx}_{miny}_{maxx}_{maxy}.tif

WMS GetMap Request:

params = {
    'SERVICE': 'WMS',
    'VERSION': '1.3.0',
    'REQUEST': 'GetMap',
    'LAYERS': 'ELEVATION.ELEVATIONGRIDCOVERAGE.HIGHRES',
    'FORMAT': 'image/geotiff',
    'BBOX': f'{minx},{miny},{maxx},{maxy}',
    'WIDTH': 1000,
    'HEIGHT': 1000,
    'CRS': 'EPSG:2154'
}

Performance:

Method	First Access	Subsequent Access	Data Quality
WMS + Cache	~2-5 seconds	<0.1 seconds	Good (rendered)
Local files	~0.1 seconds	~0.1 seconds	Best (raw)

Cache Management

Cache location:

Default: .cache/ign_lidar/rge_alti/
Files: rge_alti_2154_635000_6856000_636000_6857000.tif

Cache size:

Each 1km² tile at 1m resolution ≈ 4-8 MB (compressed)
100 tiles ≈ 400-800 MB

Clearing cache:

# Clear all cached tiles
rm -rf .cache/ign_lidar/rge_alti/*.tif

# Clear specific bbox
rm .cache/ign_lidar/rge_alti/rge_alti_2154_635000_*.tif

Programmatic cleanup:

from pathlib import Path
import time

cache_dir = Path(".cache/ign_lidar/rge_alti")
max_age_days = 30
now = time.time()

for cache_file in cache_dir.glob("*.tif"):
    age_days = (now - cache_file.stat().st_mtime) / 86400
    if age_days > max_age_days:
        cache_file.unlink()

Configuration

Complete Example

# RGE ALTI Data Source
data_sources:
  rge_alti:
    enabled: true
    use_wcs: true # Enable WMS
    resolution: 1.0 # 1m resolution
    cache_enabled: true # Strongly recommended
    cache_dir: .cache/rge_alti # Cache location
    cache_ttl_days: 90 # Cache expiration
    local_dtm_dir: null # Optional: local files

# Ground Augmentation
ground_truth:
  rge_alti:
    augment_ground: true
    augmentation_strategy: "intelligent" # gaps|intelligent|full
    augmentation_spacing: 2.0 # Grid spacing (meters)
    min_distance_to_existing: 0.5 # Min distance to LiDAR ground
    max_elevation_difference: 5.0 # Max elevation difference

# Height Computation
features:
  height_method: "dtm" # Use DTM for height
  use_rge_alti_for_height: true
  compute_height_above_ground: true
  compute_height_local: true # Also compute local (comparison)

# Classification Thresholds (with height_above_ground)
classification:
  thresholds:
    roads:
      max_height_above_ground: 0.5 # Excludes trees
      min_planarity: 0.7
    sports:
      max_height_above_ground: 2.0 # Surface + low equipment
      min_planarity: 0.6
    cemeteries:
      max_height_above_ground: 2.5 # Tombs + monuments
    parking:
      max_height_above_ground: 0.5 # Same as roads
    water:
      min_height_above_ground: -0.5 # Below DTM
      max_height_above_ground: 0.3 # Surface + banks

Minimal Configuration

data_sources:
  rge_alti:
    enabled: true
    use_wcs: true
    cache_enabled: true

ground_truth:
  rge_alti:
    augment_ground: true

Production Configuration

data_sources:
  rge_alti:
    enabled: true
    use_wcs: false # Disable WMS
    local_dtm_dir: /data/rge_alti_tiles # Use pre-downloaded files
    cache_dir: /var/cache/rge_alti # Persistent cache
    resolution: 1.0

ground_truth:
  rge_alti:
    augment_ground: true
    augmentation_strategy: "intelligent"
    augmentation_spacing: 1.5 # Denser grid

Implementation Details

Processing Pipeline Integration

RGE ALTI augmentation occurs early in the pipeline:

# Pipeline order in processor.py
def _process_tile_core():
    # 1. Load LiDAR points
    points, classification = load_laz_file(tile_path)

    # 1a. Augment ground with RGE ALTI (BEFORE features)
    if rge_alti_enabled and augment_ground:
        points, classification = self._augment_ground_with_dtm(
            points, classification, bbox
        )

    # 2. Compute features (on ALL points including synthetic)
    features = compute_geometric_features(points)

    # 3. Compute height_above_ground (using DTM)
    height_above_ground = points[:, 2] - dtm.sample_elevation(points[:, :2])

    # 4. Classification
    classification = classify_ground_truth(points, features, height_above_ground)

    # 5. Save
    save_enriched_laz(points, classification, features)

BD TOPO Classification Adaptation

With height_above_ground, BD TOPO classification is more accurate:

Before (local height):

# Roads: includes roadside trees (height from local minimum)
road_mask = (height <= 2.0) & (planarity >= 0.7)

After (DTM height):

# Roads: only surface points (height above DTM)
road_mask = (height_above_ground <= 0.5) & (planarity >= 0.7)

Classification thresholds:

Surface	Old (height)	New (height_above_ground)	Improvement
Roads	≤ 2.0m	≤ 0.5m	Excludes trees/hedges
Railways	≤ 2.0m	≤ 0.8m	Rails + ballast only
Sports	Not filtered	≤ 2.0m	Surface + low equipment
Cemeteries	Not filtered	≤ 2.5m	Tombs + monuments
Parking	Not filtered	≤ 0.5m	Same as roads
Water	Not filtered	-0.5 to 0.3m	Surface + banks

Vegetation Reclassification

Automatically detects and reclassifies vegetation above surfaces:

# In reclassifier.py
def reclassify_vegetation_above_surfaces(points, labels, features, ground_truth):
    """
    Identifies points that are:
    1. Inside BD TOPO polygon (road, sport, cemetery, parking)
    2. Elevated: height_above_ground > 2.0m (configurable)
    3. Vegetation: NDVI > 0.3 (if available)

    Action: Reclassify to vegetation based on height:
    - Low vegetation (3): <= 3m
    - Medium vegetation (4): 3-10m
    - High vegetation (5): > 10m
    """
    for feature_type in ['roads', 'sports', 'cemeteries', 'parking']:
        # Points inside BD TOPO polygons
        in_polygon_mask = labels == feature_asprs_class[feature_type]

        # High above ground + vegetation signature
        vegetation_mask = (
            in_polygon_mask &
            (height_above_ground > 2.0) &
            (ndvi > 0.3 if 'ndvi' in features else True)
        )

        # Reclassify by height
        labels[vegetation_mask & (height_above_ground <= 3)] = 3   # Low veg
        labels[vegetation_mask & (height_above_ground <= 10)] = 4  # Medium veg
        labels[vegetation_mask & (height_above_ground > 10)] = 5   # High veg

Migration Guide

From Old WCS Service

WCS Deprecated

The old WCS endpoint (wxs.ign.fr) no longer works. Migrate to WMS with caching.

Old code (broken):

fetcher = RGEALTIFetcher(
    use_wcs=True,
    api_key="pratique"  # No longer needed
)

New code (working):

fetcher = RGEALTIFetcher(
    use_wcs=True,  # Actually enables WMS now
    cache_dir=".cache/rge_alti"
)

Manual Download Option

For production or offline processing, download RGE ALTI tiles:

Visit IGN Download Portal
Navigate to Altimetry → RGE ALTI
Download tiles covering your area
Configure local directory:

fetcher = RGEALTIFetcher(
    local_dtm_dir="/path/to/rge_alti_tiles",
    cache_dir=".cache/rge_alti",
    use_wcs=False  # Disable WMS
)

Alternative Data Sources

If RGE ALTI unavailable:

Source	Resolution	Coverage	Access
LiDAR HD MNT	1m	Limited (newer areas)	Download
BD ALTI	25m	Complete France	Download
EU-DEM	25-30m	Europe	Copernicus

Best Practices

1. Always Enable Caching

# ✅ GOOD
data_sources:
  rge_alti:
    cache_enabled: true
    cache_dir: /data/cache/rge_alti  # Persistent location

# ❌ BAD
data_sources:
  rge_alti:
    cache_enabled: false  # Every fetch downloads from WMS

Benefits:

5-10× speedup on repeated runs
Works offline after first fetch
Reduces server load
Protects against service interruptions

2. Use Local Files for Production

# Production: Reliable, fast, offline-capable
fetcher = RGEALTIFetcher(
    local_dtm_dir="/data/rge_alti",
    use_wcs=False
)

# Development: Convenient, automatic
fetcher = RGEALTIFetcher(
    cache_dir=".cache/rge_alti",
    use_wcs=True
)

3. Monitor Cache Size

# Check cache size
du -sh .cache/ign_lidar/rge_alti

# Implement cleanup for long-running applications
find .cache/ign_lidar/rge_alti -name "*.tif" -mtime +30 -delete

4. Validate Results

# After augmentation
print(f"Original points: {n_original:,}")
print(f"Synthetic points: {n_synthetic:,}")
print(f"Total points: {n_total:,}")
print(f"Augmentation: +{100*n_synthetic/n_original:.1f}%")

# Sanity checks
assert n_synthetic > 0, "No synthetic points added"
assert n_synthetic < n_original, "Too many synthetic points"

5. Tune Augmentation Strategy

Use Case	Strategy	Spacing	Result
Conservative	`gaps`	3.0m	Only fill obvious gaps
Balanced	`intelligent`	2.0m	Buildings + vegetation (recommended)
Maximum	`full`	1.0m	Complete coverage everywhere

Troubleshooting

Problem: WMS Fetch Failures

Symptoms:

[WARNING] WMS fetch failed: HTTPError 400
[ERROR] Failed to fetch DTM for bbox

Solutions:

Check network:

curl https://data.geopf.fr/wms-r/wms?REQUEST=GetCapabilities

Reduce bbox size:

# ❌ Too large (may timeout)
bbox = (0, 0, 10000, 10000)

# ✅ Reasonable size
bbox = (635000, 6856000, 636000, 6857000)  # 1km²

Enable caching:
```
cache_enabled: true
```
Use local files:
```
local_dtm_dir: /data/rge_alti_tiles
```

Problem: No Synthetic Points Added

Symptoms:

[INFO] ℹ️  No ground points added (sufficient existing coverage)

Causes:

Already dense ground coverage
Augmentation disabled
Strategy too conservative

Solutions:

# Try more aggressive strategy
ground_truth:
  rge_alti:
    augmentation_strategy: "full" # Was "gaps"
    augmentation_spacing: 1.0 # Was 2.0

Problem: Cache Not Being Used

Symptoms:

[INFO] Fetching DTM from IGN WMS: ...  # Every time

Solutions:

Verify cache directory:

assert Path(cache_dir).exists()
assert os.access(cache_dir, os.W_OK)

Check file permissions:

ls -la .cache/ign_lidar/rge_alti/
chmod 755 .cache/ign_lidar/rge_alti/

Use consistent bbox:

# Round bbox to avoid slight differences
bbox = tuple(round(v, 1) for v in bbox)

Problem: Poor DTM Quality

Cause: WMS rendering/compression

Solution: Use high-quality local files:

Download from IGN
Use raw GeoTIFF files
Configure local_dtm_dir

DTM Processing Guide - Complete DTM enrichment reference
Ground Truth Classification - Using BD TOPO data
Configuration Guide - Complete configuration reference
Height Features - Height calculation methods

References

IGN RGE ALTI - Official product page
Géoplateforme - New IGN platform
WMS GetCapabilities - Service capabilities
Download Portal - Manual download

Last updated: October 21, 2025

Overview​

Key Specifications​

Table of Contents​

Quick Start​

Basic Setup​

Python Usage​

Integration Methods​

1. Ground Point Augmentation​

2. Accurate Height Computation​

3. Enhanced Classification​

WMS Service & Caching​

IGN Géoplateforme Migration​

Data Source Priority​

WMS with Automatic Caching​

Cache Management​

Configuration​

Complete Example​

Minimal Configuration​

Production Configuration​

Implementation Details​

Processing Pipeline Integration​

BD TOPO Classification Adaptation​

Vegetation Reclassification​

Migration Guide​

From Old WCS Service​

Manual Download Option​

Alternative Data Sources​

Best Practices​

1. Always Enable Caching​

2. Use Local Files for Production​

3. Monitor Cache Size​

4. Validate Results​

5. Tune Augmentation Strategy​

Troubleshooting​

Problem: WMS Fetch Failures​

Problem: No Synthetic Points Added​

Problem: Cache Not Being Used​

Problem: Poor DTM Quality​

Related Documentation​

References​

Overview

Key Specifications

Table of Contents

Quick Start

Basic Setup

Python Usage

Integration Methods

1. Ground Point Augmentation

2. Accurate Height Computation

3. Enhanced Classification

WMS Service & Caching

IGN Géoplateforme Migration

Data Source Priority

WMS with Automatic Caching

Cache Management

Configuration

Complete Example

Minimal Configuration

Production Configuration

Implementation Details

Processing Pipeline Integration

BD TOPO Classification Adaptation

Vegetation Reclassification

Migration Guide

From Old WCS Service

Manual Download Option

Alternative Data Sources

Best Practices

1. Always Enable Caching

2. Use Local Files for Production

3. Monitor Cache Size

4. Validate Results

5. Tune Augmentation Strategy

Troubleshooting

Problem: WMS Fetch Failures

Problem: No Synthetic Points Added

Problem: Cache Not Being Used

Problem: Poor DTM Quality

Related Documentation

References