Features Overview

IGN LiDAR HD Dataset provides comprehensive tools for processing high-density LiDAR data into machine learning-ready datasets with advanced building feature extraction.

Core Features

🏗️ Building Component Classification

Advanced classification system for identifying building components with high precision.

Components Identified:

• Roofs: Pitched, flat, complex geometries
• Walls: Facades, load-bearing, curtain walls
• Ground: Terrain, courtyards, foundations
• Details: Chimneys, dormers, balconies

Key Capabilities:

from ign_lidar import BuildingProcessor

processor = BuildingProcessor()
components = processor.classify_components(
    point_cloud,
    min_wall_height=2.0,
    roof_detection_threshold=0.8
)

📐 Geometric Feature Extraction

Comprehensive geometric analysis for each point and building segment.

Extracted Features:

• Planarity: Surface flatness measurement
• Linearity: Edge and linear structure detection
• Sphericity: 3D shape compactness
• Normal Vectors: Surface orientation
• Curvature: Local geometry analysis

Usage:

features = processor.extract_geometric_features(
    points,
    neighborhood_size=1.0,
    feature_types=['planarity', 'linearity', 'normal_vectors']
)

🎨 RGB Augmentation

Integration with IGN orthophotos for color-enhanced point clouds.

Capabilities:

• Color Mapping: Precise RGB assignment from orthophotos
• Texture Analysis: Surface material classification
• Multi-spectral: Support for infrared channels
• Quality Assessment: Color accuracy validation

Example:

rgb_processor = RGBProcessor()
colored_cloud = rgb_processor.augment_with_rgb(
    point_cloud,
    orthophoto_path="ortho.tif",
    interpolation_method="bilinear"
)

⚡ GPU Acceleration

High-performance computing with CUDA support for large-scale processing.

Accelerated Operations:

• Feature extraction: 10-15x speedup
• RGB augmentation: 8-12x speedup
• Point cloud filtering: 5-8x speedup
• Batch processing: Efficient memory management

Configuration:

processor = Processor(
    use_gpu=True,
    gpu_memory_fraction=0.7,
    batch_size=100000
)

Advanced Features

🏛️ Architectural Style Recognition

Automatic detection and classification of architectural styles and periods.

Supported Styles:

• Traditional French regional architecture
• Haussmanian Parisian buildings
• Contemporary structures
• Industrial buildings

Regional Adaptation:

style_analyzer = ArchitecturalAnalyzer(
    region="ile_de_france",
    historical_period="haussmanian",
    building_type="residential"
)

📊 LOD3 Generation

Level of Detail 3 (LOD3) building models with architectural details.

Generated Elements:

• Detailed roof structures
• Window and door openings
• Balconies and architectural features
• Accurate building footprints

🔄 Pipeline Configuration

Flexible processing pipelines for different use cases and datasets.

Pipeline Types:

• Full Pipeline: Complete processing with all features
• Fast Pipeline: Optimized for speed, core features only
• Custom Pipeline: User-defined feature selection
• Batch Pipeline: Efficient multi-tile processing

Configuration Example:

pipeline:
  name: "urban_analysis"
  stages:
    - download
    - preprocess
    - extract_features
    - classify_buildings
    - generate_patches

  settings:
    feature_extraction:
      geometric_features: true
      architectural_analysis: true
    gpu_acceleration: true
    output_format: "h5"

Processing Workflows

Standard Workflow

GPU-Accelerated Workflow

Feature Categories

Geometric Features

Feature	Description	Use Case
Planarity	Surface flatness	Roof/wall detection
Linearity	Edge strength	Building outlines
Sphericity	3D compactness	Architectural details
Height	Elevation analysis	Building stories
Normal Z	Vertical orientation	Roof slope analysis

Architectural Features

Feature	Description	Application
Wall Detection	Vertical surface identification	Facade analysis
Roof Analysis	Roof type classification	Building modeling
Opening Detection	Windows/doors	Detailed LOD3
Corner Detection	Building corners	Geometric accuracy
Overhang Analysis	Balconies/eaves	Architectural details

Color Features (RGB)

Feature	Description	Benefit
Material Classification	Surface material ID	Texture mapping
Color Histograms	Color distribution	Building style
Texture Analysis	Surface patterns	Material properties
Shadow Detection	Occlusion analysis	Quality assessment

Performance Metrics

Processing Speed

Dataset Size	CPU Time	GPU Time	Speedup
10M points	15 min	2 min	7.5x
50M points	75 min	8 min	9.4x
100M points	150 min	15 min	10x

Memory Usage

• CPU Processing: ~8GB RAM for 50M points
• GPU Processing: ~4GB GPU + 4GB RAM for 50M points
• Batch Mode: Configurable memory footprint

Accuracy Metrics

• Building Classification: 94% accuracy on test dataset
• Component Classification: 89% accuracy (roof/wall/ground)
• Feature Extraction: Sub-meter geometric precision

Output Formats

Point Cloud Formats

• LAS/LAZ: Industry standard with custom fields
• PLY: Research-friendly with color support
• HDF5: High-performance with metadata
• NPZ: NumPy arrays for Python workflows

Extracted Data

• Features CSV: Tabular feature data
• Patches H5: ML-ready training patches
• Metadata JSON: Processing parameters and stats
• Quality Reports: Validation and accuracy metrics

Integration Examples

Machine Learning Pipeline

# Prepare training data
processor = Processor(output_format="patches")
training_data = processor.generate_ml_patches(
    tile_list,
    patch_size=32,
    overlap=0.5,
    augmentation=True
)

# Train model
model = train_building_classifier(training_data)

GIS Integration

# Export for GIS analysis
processor.export_to_shapefile(
    buildings_data,
    output_path="buildings.shp",
    include_attributes=['height', 'roof_type', 'material']
)

Visualization

# Generate 3D visualization
visualizer = Visualizer3D()
visualizer.render_buildings(
    point_cloud,
    building_labels,
    color_by='classification',
    show_features=True
)

Quality Assurance

Validation Methods

• Ground Truth Comparison: Manual survey validation
• Cross-Validation: Multiple processing runs
• Statistical Analysis: Feature distribution analysis
• Visual Inspection: 3D rendering verification

Quality Metrics

• Completeness: Percentage of buildings detected
• Correctness: Classification accuracy
• Geometric Accuracy: Coordinate precision
• Feature Quality: Feature extraction reliability

Documentation Links

• Installation Guide - Setup instructions
• API Reference - Detailed API documentation
• Performance Guide - Optimization techniques
• Examples - Code examples and tutorials
• Troubleshooting - Common issues and solutions

Getting Started

1. Install the package: pip install ign-lidar-hd
2. Download sample data: Use built-in downloader
3. Run basic processing: Follow quick-start guide
4. Explore features: Try different processing options
5. Optimize for your use case: Configure pipelines

For detailed getting started instructions, see the Quick Start Guide.