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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
)