ML4Earth Hackathon 2024 - Starter Kit

• Link to the hackathon on the devpost Platform: https://ml4earth24.devpost.com/
• Slack workspace for communication during the hackathon: https://join.slack.com/t/ml4earthworkspace/shared_invite/zt-2q7pseobr-JX302q4~tl3dbYoFCWPq9Q

In this Starter Pack

• A Jupyter Notebook to get you started
• The kick-off slides containing organizational information about the hackathon

About the Hackathon

Foundation models are rapidly changing the way we approach machine learning tasks. Are you ready to dive into the future of machine learning? This is your chance to make a real impact! In our ML4Earth Foundation Model Hackathon, you'll unleash the power of cutting-edge foundation models to create high-resolution maps from aerial imagery.

What to Build

In this Hackathon, your mission is to harness the power of foundation models to produce high-resolution, detailed maps from aerial imagery. Here's what you'll need to focus on:

1. High-Resolution Mapping:

• Goal: Create detailed, accurate maps from aerial imagery.
• Scope: Focus on urban planning, environmental monitoring, and other real-world applications where high-resolution mapping can make a difference.

2. Model Selection & Fine-Tuning:

• Choice: Select a foundation model that best fits the task—whether it’s a transformer, CNN, or any other advanced architecture.
• Customization: Fine-tune your chosen model using the curated datasets we provide to enhance its performance for your specific mapping task.

3. Data Integration:

• Training Data: Utilize our comprehensive training dataset to teach your model.
• Testing Data: Validate your model's accuracy and efficiency with our testing dataset.

4. Impact & Usability: Real-World Applications: Think about how your project can be applied in real-world scenarios. Highlight its potential impact on industries like agriculture, urban development, disaster response, and more. Scalability: Ensure your solution is scalable and can handle large datasets efficiently.

What to Submit

• Performance Metrics: An analysis of your model’s performance, including accuracy, speed, and any other relevant metrics.
• High-Resolution Maps: The final output maps generated by your model.
• Demo/Pitch Video: A short video demonstrating your project's capabilities and showcasing its features.
• Source Code: A repository containing all the code used in your project, with clear instructions on how to run it.