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Change Detection Models for National Infrastructure Monitoring

This repository contains a collection of Fine-tuned change detection models developed by Team-1 from San Jose State University as part of the National Infrastructure Monitoring project.

Models and Contributors

Our team has implemented several state-of-the-art change detection models:

1. ChangeViT: Built by Nihar

   • Combines Vision Transformer (ViT) and CNN architectures
   • Excels at detecting both large-scale and fine-grained changes
   • Nihar's LinkedIn

2. BITCD: Developed by Charishma

   • Uses a transformer-based approach for advanced change detection
   • Processes images as compact token sets for improved efficiency
   • Charishma's LinkedIn

3. ChangeFormer: Implemented by Keerthana

   • Transformer-based architecture for satellite imagery change detection
   • Captures long-range spatial and temporal dependencies
   • Keerthana's LinkedIn

4. Multi-Modal Adaptation Network: Content generation by Anbu

   • Combines optical and SAR imagery for robust change detection
   • Utilizes domain adaptation to align features from different image types
   • Anbu's LinkedIn

5. Siamese Nested UNet: Developed by Harika

   • Combines Siamese network and U-Net architectures
   • Excels at image comparison tasks for change detection
   • Harika's LinkedIn

Key Features

• Advanced change detection capabilities for high-resolution satellite imagery
• Utilization of transformer-based approaches for capturing long-range relationships
• Efficient processing of large-scale datasets
• Combination of multiple imaging modalities for improved accuracy
• Scalability to handle various image sizes and resolutions

These models represent cutting-edge approaches in remote sensing and change detection, specifically tailored for national infrastructure monitoring applications.