Historical Analysis

Tools and methods for analyzing temporal changes in LiDAR datasets and detecting urban evolution patterns.

Overview

Historical analysis capabilities enable researchers to track changes in urban environments over time using multi-temporal LiDAR datasets. These tools support urban planning, environmental monitoring, and heritage preservation applications.

Temporal Analysis Classes

ChangeDetector

Core class for detecting changes between LiDAR datasets from different time periods.

from ign_lidar.temporal import ChangeDetector

detector = ChangeDetector(
    resolution=1.0,  # Analysis grid resolution in meters
    height_threshold=0.5,  # Minimum height change to detect
    confidence_threshold=0.8
)

# Detect changes between two datasets
changes = detector.detect_changes(
    dataset_t1="lidar_2020.las",
    dataset_t2="lidar_2024.las",
    change_types=["construction", "demolition", "vegetation"]
)

Change Detection Methods

Building Change Detection

# Detect new constructions and demolitions
building_changes = detector.detect_building_changes(
    buildings_t1=buildings_2020,
    buildings_t2=buildings_2024,
    match_threshold=0.7  # Geometric similarity threshold
)

# Results structure
{
    'new_buildings': List[Building],      # Newly constructed
    'demolished_buildings': List[Building], # Demolished structures
    'modified_buildings': List[Tuple[Building, Building]], # Changed buildings
    'stable_buildings': List[Building],    # Unchanged buildings
    'change_statistics': Dict             # Summary statistics
}

Vegetation Change Analysis

# Analyze vegetation changes
vegetation_changes = detector.analyze_vegetation_changes(
    points_t1=points_2020,
    points_t2=points_2024,
    vegetation_threshold=2.0,  # Minimum vegetation height
    growth_threshold=1.5       # Significant growth threshold
)

# Returns
{
    'deforestation_areas': List[Polygon],  # Areas of tree removal
    'afforestation_areas': List[Polygon],  # New vegetation areas
    'growth_areas': List[Polygon],         # Significant vegetation growth
    'canopy_changes': Dict,                # Canopy cover statistics
    'biomass_changes': Dict                # Estimated biomass changes
}

Urban Densification Analysis

# Analyze urban development patterns
densification = detector.analyze_urban_densification(
    datasets=[lidar_2015, lidar_2020, lidar_2024],
    analysis_zones=city_districts,
    indicators=["building_density", "height_growth", "footprint_ratio"]
)

# Per-zone analysis results
for zone, analysis in densification.items():
    print(f"Zone {zone}:")
    print(f"  Building density change: {analysis['density_change']:.2f}%")
    print(f"  Average height increase: {analysis['height_increase']:.1f}m")
    print(f"  New footprint area: {analysis['new_area']:.1f} m²")

TemporalProcessor

Specialized processor for handling time-series LiDAR data.

from ign_lidar.temporal import TemporalProcessor

processor = TemporalProcessor(
    temporal_resolution="annual",  # "monthly", "annual", "custom"
    alignment_method="icp",        # Point cloud alignment method
    normalization=True             # Normalize elevation references
)

# Process temporal dataset
time_series = processor.process_time_series(
    datasets=[
        {"path": "lidar_2020.las", "date": "2020-06-15"},
        {"path": "lidar_2021.las", "date": "2021-06-20"},
        {"path": "lidar_2022.las", "date": "2022-06-18"},
        {"path": "lidar_2023.las", "date": "2023-06-25"},
        {"path": "lidar_2024.las", "date": "2024-06-22"}
    ]
)

Temporal Interpolation

# Interpolate missing temporal data
interpolated = processor.temporal_interpolation(
    time_series=time_series,
    target_dates=["2020-12-31", "2021-12-31"],
    method="cubic_spline"
)

Trend Analysis

# Analyze temporal trends
trends = processor.analyze_trends(
    time_series=time_series,
    variables=["building_height", "vegetation_coverage", "ground_level"],
    trend_methods=["linear", "polynomial", "seasonal"]
)

# Trend results
{
    'building_height': {
        'trend': 'increasing',
        'rate': 0.3,  # meters per year
        'confidence': 0.95,
        'seasonal_pattern': True
    },
    'vegetation_coverage': {
        'trend': 'stable',
        'seasonal_amplitude': 0.15,
        'peak_month': 'June'
    }
}

Heritage Monitoring

HeritageMonitor

Specialized tools for monitoring heritage buildings and historical sites.

from ign_lidar.heritage import HeritageMonitor

heritage_monitor = HeritageMonitor(
    sensitivity="high",  # Change detection sensitivity
    preserve_details=True,
    documentation_level="comprehensive"
)

# Monitor heritage site changes
heritage_analysis = heritage_monitor.analyze_heritage_changes(
    baseline="heritage_site_2020.las",
    current="heritage_site_2024.las",
    heritage_boundaries=monument_polygons,
    protected_features=["facades", "rooflines", "structural_elements"]
)

Structural Integrity Assessment

# Assess structural changes in heritage buildings
integrity_assessment = heritage_monitor.assess_structural_integrity(
    building_id="monument_001",
    time_series=building_time_series,
    critical_zones=["load_bearing_walls", "roof_structure"],
    tolerance_levels={
        "settlement": 0.02,      # 2cm maximum settlement
        "deformation": 0.01,     # 1cm maximum deformation
        "crack_width": 0.005     # 5mm maximum crack width
    }
)

# Alert system for critical changes
if integrity_assessment.has_critical_changes():
    alerts = integrity_assessment.get_critical_alerts()
    for alert in alerts:
        print(f"🚨 CRITICAL: {alert.description}")
        print(f"   Location: {alert.coordinates}")
        print(f"   Severity: {alert.severity_level}")

Documentation Generation

# Generate heritage monitoring reports
report = heritage_monitor.generate_monitoring_report(
    site_id="historical_district_001",
    analysis_results=heritage_analysis,
    report_type="conservation",
    include_3d_models=True,
    include_recommendations=True
)

# Export report in multiple formats
report.save("heritage_report.pdf")
report.save("heritage_report.html")
report.export_3d_models("models/")

Urban Evolution Analysis

UrbanEvolutionAnalyzer

Comprehensive analysis of urban development patterns over time.

from ign_lidar.urban_evolution import UrbanEvolutionAnalyzer

evolution_analyzer = UrbanEvolutionAnalyzer(
    analysis_scale="neighborhood",  # "full", "block", "neighborhood", "city"
    temporal_window="5_years",
    evolution_indicators=["density", "height", "morphology", "green_space"]
)

# Analyze urban evolution patterns
evolution_patterns = evolution_analyzer.analyze_evolution_patterns(
    city_datasets=temporal_datasets,
    urban_zones=planning_zones,
    reference_year=2020
)

Development Pattern Recognition

# Identify development patterns
development_patterns = evolution_analyzer.identify_development_patterns(
    evolution_data=evolution_patterns,
    pattern_types=[
        "sprawl",           # Urban sprawl expansion
        "densification",    # Urban densification
        "regeneration",     # Urban regeneration
        "gentrification",   # Neighborhood gentrification
        "green_development" # Green space integration
    ]
)

# Pattern classification results
for pattern in development_patterns:
    print(f"Pattern: {pattern.type}")
    print(f"Area: {pattern.area_km2:.2f} km²")
    print(f"Intensity: {pattern.intensity_score:.2f}")
    print(f"Timeframe: {pattern.start_date} to {pattern.end_date}")
    print(f"Confidence: {pattern.confidence:.2f}")

Sustainability Metrics

# Calculate urban sustainability indicators
sustainability_metrics = evolution_analyzer.calculate_sustainability_metrics(
    evolution_data=evolution_patterns,
    metrics=[
        "green_space_ratio",
        "building_energy_efficiency",
        "urban_heat_island",
        "walkability_index",
        "density_optimization"
    ]
)

# Generate sustainability dashboard
dashboard = evolution_analyzer.create_sustainability_dashboard(
    metrics=sustainability_metrics,
    target_values=sustainability_targets,
    visualization_style="interactive"
)

Climate Impact Analysis

ClimateChangeAnalyzer

Analyze the impact of climate change on urban infrastructure using temporal LiDAR data.

from ign_lidar.climate import ClimateChangeAnalyzer

climate_analyzer = ClimateChangeAnalyzer(
    climate_variables=["temperature", "precipitation", "wind"],
    impact_indicators=["flooding", "erosion", "vegetation_stress"]
)

# Analyze climate impacts
climate_impacts = climate_analyzer.analyze_climate_impacts(
    lidar_time_series=temporal_datasets,
    climate_data=weather_station_data,
    vulnerability_maps=flood_risk_maps
)

Flood Impact Assessment

# Assess flood impacts on urban areas
flood_assessment = climate_analyzer.assess_flood_impacts(
    pre_flood="lidar_before_flood.las",
    post_flood="lidar_after_flood.las",
    flood_extent=flood_boundary,
    infrastructure_layers=["roads", "buildings", "utilities"]
)

# Damage quantification
damage_report = {
    'affected_buildings': flood_assessment.building_damage_count,
    'road_damage_km': flood_assessment.road_damage_length,
    'estimated_cost': flood_assessment.estimated_repair_cost,
    'recovery_time_estimate': flood_assessment.recovery_timeframe
}

Visualization and Reporting

TemporalVisualizer

Create visualizations for temporal analysis results.

from ign_lidar.visualization import TemporalVisualizer

visualizer = TemporalVisualizer(
    style="scientific",
    color_scheme="temporal",
    animation_quality="high"
)

# Create temporal change animation
animation = visualizer.create_temporal_animation(
    time_series_data=time_series,
    change_overlay=change_detection_results,
    animation_type="morphing",  # "morphing", "overlay", "side_by_side"
    frame_rate=2,  # frames per second
    duration=10    # seconds
)

animation.save("urban_evolution.mp4")

Interactive Dashboards

# Create interactive temporal analysis dashboard
dashboard = visualizer.create_temporal_dashboard(
    datasets=temporal_datasets,
    analysis_results=[changes, trends, patterns],
    dashboard_components=[
        "timeline_slider",
        "change_heatmap",
        "statistics_panel",
        "3d_viewer",
        "export_tools"
    ]
)

# Deploy dashboard
dashboard.deploy(
    host="localhost",
    port=8080,
    title="Urban Evolution Analysis Dashboard"
)

Report Generation

from ign_lidar.reporting import TemporalReport

# Generate comprehensive temporal analysis report
report = TemporalReport(
    title="Urban Development Analysis 2020-2024",
    authors=["Urban Planning Department"],
    analysis_data={
        'change_detection': change_results,
        'trend_analysis': trend_results,
        'heritage_monitoring': heritage_results,
        'sustainability_metrics': sustainability_data
    }
)

# Configure report sections
report.add_executive_summary()
report.add_methodology_section()
report.add_results_section(include_visualizations=True)
report.add_recommendations_section()
report.add_appendices(include_raw_data=False)

# Export in multiple formats
report.export_pdf("temporal_analysis_report.pdf")
report.export_html("temporal_analysis_report.html")
report.export_data("temporal_analysis_data.json")

Advanced Applications

Predictive Modeling

from ign_lidar.prediction import UrbanPredictionModel

# Train predictive model on historical data
prediction_model = UrbanPredictionModel(
    model_type="neural_network",  # "linear", "random_forest", "neural_network"
    features=["density_trend", "height_growth", "land_use_changes"],
    prediction_horizon="5_years"
)

prediction_model.train(
    training_data=historical_evolution_data,
    validation_split=0.2
)

# Generate future development predictions
predictions = prediction_model.predict_development(
    current_state=current_urban_state,
    scenario_parameters={
        'population_growth': 0.02,  # 2% annual growth
        'economic_growth': 0.03,    # 3% annual growth
        'planning_constraints': planning_regulations
    }
)

# Prediction results
{
    'predicted_building_density': 1250,  # buildings/km²
    'predicted_average_height': 18.5,    # meters
    'confidence_interval': (16.2, 20.8), # 95% confidence
    'uncertainty_factors': ['economic_volatility', 'policy_changes']
}

Multi-Scale Analysis

from ign_lidar.multiscale import MultiScaleTemporalAnalyzer

# Perform analysis at multiple spatial scales
multiscale_analyzer = MultiScaleTemporalAnalyzer(
    scales=["full", "block", "neighborhood", "city"],
    scale_interactions=True
)

multiscale_results = multiscale_analyzer.analyze_multiscale_changes(
    temporal_data=time_series_data,
    spatial_hierarchies=administrative_boundaries
)

# Cross-scale interaction analysis
interactions = multiscale_analyzer.analyze_scale_interactions(
    multiscale_results=multiscale_results,
    interaction_types=["top_down", "bottom_up", "lateral"]
)

Integration and Workflows

Automated Monitoring Systems

from ign_lidar.monitoring import AutomatedMonitoringSystem

# Set up automated temporal monitoring
monitoring_system = AutomatedMonitoringSystem(
    data_sources=["ign_api", "local_storage"],
    monitoring_frequency="monthly",
    alert_thresholds={
        'rapid_construction': 100,    # buildings per month
        'significant_demolition': 50, # buildings per month
        'vegetation_loss': 0.1        # 10% coverage loss
    }
)

# Configure monitoring zones
monitoring_system.add_monitoring_zone(
    zone_id="downtown_district",
    geometry=downtown_boundary,
    priority="high",
    notification_recipients=["planning@city.gov"]
)

# Start automated monitoring
monitoring_system.start()

API Integration

from ign_lidar.api import TemporalAnalysisAPI

# RESTful API for temporal analysis services
api = TemporalAnalysisAPI(
    host="0.0.0.0",
    port=5000,
    authentication=True
)

# API endpoints for temporal analysis
@api.route("/analyze_changes", methods=["POST"])
def analyze_changes_endpoint():
    """Endpoint for change detection analysis."""
    request_data = api.get_request_data()

    # Perform analysis
    results = detector.detect_changes(
        dataset_t1=request_data["dataset_t1"],
        dataset_t2=request_data["dataset_t2"],
        parameters=request_data["parameters"]
    )

    return api.json_response(results)

# Start API server
api.run()

Best Practices

Data Quality Assurance

# Implement quality checks for temporal analysis
from ign_lidar.quality import TemporalQualityController

quality_controller = TemporalQualityController(
    alignment_tolerance=0.1,     # 10cm alignment tolerance
    temporal_consistency=True,    # Check temporal consistency
    completeness_threshold=0.95   # 95% data completeness required
)

# Validate temporal dataset quality
quality_report = quality_controller.validate_temporal_dataset(
    datasets=temporal_datasets,
    reference_standards="ign_specifications"
)

if quality_report.has_issues():
    print("⚠️  Quality issues detected:")
    for issue in quality_report.issues:
        print(f"   - {issue.description}")
        print(f"     Severity: {issue.severity}")
        print(f"     Recommendation: {issue.recommendation}")

Performance Optimization

# Optimize temporal analysis performance
from ign_lidar.optimization import TemporalOptimizer

optimizer = TemporalOptimizer(
    caching_strategy="aggressive",
    parallel_processing=True,
    memory_management="adaptive"
)

# Apply optimizations
optimized_detector = optimizer.optimize_change_detector(
    detector=detector,
    expected_data_size="large",  # "small", "medium", "large"
    processing_time_limit=3600   # 1 hour maximum
)

Related Documentation

• Regional Processing
• Architectural Styles
• Building Features
• Visualization Guide