Change Detection

Change detection is a geospatial analysis technique used to identify differences in imagery or sensor data collected from the same area at different times. It’s widely applied in fields like urban development, environmental monitoring, disaster management, and land use analysis.

The process involves comparing two or more remote sensing images to detect alterations in physical, structural, or natural features. These changes might include vegetation growth, construction activity, flooding, or deforestation.

Change detection typically uses:

• Image differencing or spectral analysis
• Pixel-based or object-based comparison
• Deep learning models trained on labeled time series data
• Temporal segmentation for event tracking
  

AI-powered change detection can automate and accelerate insights from satellite or aerial imagery, especially when trained on annotated datasets. Manual validation or semi-supervised labeling is often used to improve accuracy and reduce false positives.

Key applications of change detection:

• Disaster recovery: Damage assessment post-earthquake, fire, or hurricane
• Urbanization monitoring: Tracking city expansion, zoning changes
• Agricultural health: Detecting drought stress, harvesting activity
• Security and defense: Monitoring troop movements or illicit construction

Change detection is essential for dynamic monitoring, helping decision-makers respond to evolving real-world conditions with speed and accuracy.