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| title: README |
| emoji: 🚀 |
| colorFrom: blue |
| colorTo: pink |
| sdk: static |
| pinned: false |
| license: apache-2.0 |
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| # IHE Delft Institute for Water Education |
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| ## About Us |
| IHE Delft Institute for Water Education is the largest international graduate water education facility in the world and is based in Delft, The Netherlands. For over 60 years, the Institute has been working to develop and share knowledge and skills to address global water challenges through education, research, and capacity development projects. |
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| ## Our AI & ML Focus Areas |
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| ### 1. Flood Forecasting Guidance |
| Our flood forecasting models provide expert guidance for: |
| - Creating clear, actionable reports for stakeholders |
| - Building models with appropriate hydroinformatics boundary conditions |
| - Supporting decision-making during flood events |
| - Integrating with existing early warning systems |
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| ### 2. Water Resources Image Interpretation |
| We develop AI models to interpret images related to water resources, including: |
| - Water quality assessment from remote sensing data |
| - Reservoir and lake monitoring |
| - Groundwater resource mapping |
| - Urban water infrastructure analysis |
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| ### 3. Coastal Problem Image Interpretation |
| Our coastal-focused models address: |
| - Coastal erosion detection and prediction |
| - Sea level rise impact assessment |
| - Coral reef and coastal ecosystem health monitoring |
| - Coastal infrastructure vulnerability analysis |
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| ## Model Upload Guidelines |
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| ### How to Upload Your Fine-Tuned Models |
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| 1. **Prepare Your Model** |
| - Ensure your model is properly fine-tuned and validated |
| - Document the training process, dataset characteristics, and evaluation metrics |
| - Check that your model complies with IHE Delft's ethical AI guidelines |
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| 2. **Required Documentation** |
| - Model card with complete description of capabilities and limitations |
| - Training methodology and hyperparameters |
| - Evaluation results with appropriate metrics for the task |
| - Example usage code |
| - License information |
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| 3. **Folder Structure** |
| ``` |
| model_name/ |
| ├── README.md # Detailed model description |
| ├── model_files/ # The actual model weights and config |
| ├── example_usage.py # Example script showing how to use the model |
| ├── evaluation/ # Evaluation scripts and results |
| └── metadata.json # Model metadata |
| ``` |
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| 4. **Uploading Process** |
| - Clone the repository: `git lfs install && git clone https://huggingface.co/IHEDelft/[model_name]` |
| - Add your files: `git add .` |
| - Commit your changes: `git commit -m "Add [model_name] with documentation"` |
| - Push to Hugging Face: `git push` |
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| 5. **Post-Upload Steps** |
| - Create a Space to demonstrate your model in action |
| - Link your model to relevant datasets |
| - Share usage examples in the community discussion |
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| ## Contribution Guidelines |
| We welcome contributions from students, researchers, and partners. All contributed models should align with IHE Delft's mission of addressing water-related challenges through sustainable and ethical approaches. |
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| ## Contact |
| For questions about contributing to our Hugging Face repository, please contact https://www.un-ihe.org/people/staff/gerald-corzo-perez . |
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| *IHE Delft is committed to developing AI technologies that support sustainable water management and equitable access to water resources worldwide.* |
