Update README.md

README.md

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+# LEVIR-MCI-Trees
+## Overview
+LEVIR-MCI-Trees is a curated subset of the LEVIR-MCI dataset specifically focused on tree cover changes in urban and peri-urban environments. This dataset supports joint change detection and captioning tasks for remote sensing imagery, containing bi-temporal image pairs with pixel-level change masks and semantic descriptions.
+
+## Dataset Details
+- **Source**: Filtered subset of LEVIR-MCI dataset (Liu et al., 2024)
+- **Total Examples**: 2,305 image pairs
+- **Spatial Resolution**: 0.5m/pixel
+- **Image Size**: 256×256 pixels
+- **Temporal Range**: 5-15 years between image pairs
+- **Geographic Focus**: Urban and peri-urban areas with tree cover changes
+
+## Dataset Splits
+- **Training**: 1,518 examples (66%)
+- **Validation**: 374 examples (16%)
+- **Test**: 413 examples (18%)
+
+## Data Format
+Each example contains:
+- **Bi-temporal image pairs**: Two RGB images (Image A and Image B) captured at different time points
+- **Change mask**: Binary/multi-class segmentation mask highlighting changes to roads and buildings
+- **Captions**: Five human-annotated captions describing the observed changes from varying perspectives
+
+## Filtering Criteria
+Examples are selected from LEVIR-MCI based on caption content. An image pair is included if at least one of its five captions contains tree-related keywords: 'tree', 'trees', 'wood', 'woods', 'woodland', 'wooded', 'forest', 'forests', 'jungle', or 'jungles'.
+
+## Key Characteristics
+- **Change Coverage**: Mean 15.28%, maximum 72.79% of image area
+- **Annotation Focus**: Pixel-level annotations for roads and buildings (not trees directly)
+- **Caption Style**: Concise descriptions with diverse vocabulary and varied perspectives
+- **Object Geometry**: Regular geometric patterns characteristic of urban infrastructure and managed landscapes
+- **Image Quality**: High-resolution imagery suitable for fine-grained analysis
+
+## Use Cases
+- Remote sensing change detection in urban environments
+- Change captioning and description generation
+- Multi-task learning for vision-language models
+- Benchmarking model performance on high-resolution imagery
+- Urban forest monitoring and tree cover analysis
+- Training and evaluating interactive remote sensing agents
+
+## Limitations
+- Change masks only annotate roads and buildings, not tree cover changes directly
+- Limited to high-resolution imagery (0.5m/pixel)
+- Fixed image size of 256×256 pixels
+- Urban-focused context may not represent natural forest environments
+- Variable temporal spans between image pairs (5-15 years)
+
+## Citation
+If you use this dataset, please cite:
+```bibtex@article{brock2024forestchat,
+title={Forest-Chat: Adapting Vision-Language Agents for Interactive Forest Change Analysis},
+author={Brock, James and Zhang, Ce and Anantrasirichai, Nantheera},
+journal={Ecological Informatics},
+year={2024}
+}
+
+@article{liu2024changeagent,
+title={Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis},
+author={Liu, Chenyang and Chen, Keyan and Zhang, Haotian and Qi, Zipeng and Zou, Zhengxia and Shi, Zhenwei},
+journal={IEEE Transactions on Geoscience and Remote Sensing},
+year={2024}
+}
+```
+
+## License
+MIT License - Academic re-use purpose only
+
+## Contact
+For questions or issues regarding this dataset, please contact:
+- James Brock: james.brock@bristol.ac.uk
+- School of Computer Science, University of Bristol