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---
language:
- en
tags:
- image-classification
license: mit
datasets:
- imagenet
---
# 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](https://www.linkedin.com/in/nihar-palem-1b955a183/)
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](https://www.linkedin.com/in/sai-charishma-kurmala-080983128/)
3. **ChangeFormer**: Implemented by Keerthana
- Transformer-based architecture for satellite imagery change detection
- Captures long-range spatial and temporal dependencies
- [Keerthana's LinkedIn](https://www.linkedin.com/in/keerthana-raskatla-1573781a4/)
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](https://www.linkedin.com/in/anbuvalluvan/)
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](https://www.linkedin.com/in/harika-boyina/)
## 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. |