Synthesizing Training Data for Change Detection Models

The patent creates a universal change detection framework that can identify multiple types of changes (land use/land cover changes, urban expansion, deforestation, agricultural changes) using a single trained model. The model processes satellite imagery sequences and detects various change types without requiring separate specialized techniques for each change category, thereby improving versatility while maintaining manageable complexity.

The patent segments the training data into multiple domains representing different change types and geographic regions. By training the model on diverse, segmented data covering various change scenarios, the model learns to recognize multiple change types simultaneously, enhancing adaptability without proportionally increasing system complexity.

The patent performs preliminary actions by curating and organizing training data from multiple sources (Landsat, Sentinel-2, open street maps) before model training. Change detection samples are created in advance by comparing satellite imagery from different time periods, and training datasets are pre-assembled with annotations. This preliminary data preparation enables reliable model training without requiring extensive data collection during the modeling phase.

The patent creates synthetic training samples by copying and adapting existing satellite imagery and change detection labels from public datasets. Training data is replicated across multiple geographic regions and time periods, allowing the model to be trained on sufficient diverse data without requiring original field collection for each scenario.

The patent segments the global Earth surface into discrete geographic tiles or regions that can be processed independently and in parallel. Each region is analyzed separately using the trained model, allowing distributed processing across multiple computational units. This segmentation enables real-time global monitoring by dividing the massive processing task into manageable chunks that can be handled with available computational resources.

The patent implements change detection at strategic sampling points and key regions first, then expands to broader coverage as computational capacity allows. Rather than processing every pixel globally at maximum resolution simultaneously, the system performs partial analysis on priority areas and uses lower-resolution screening for other regions, achieving real-time detection capability while managing computational resource requirements.