Next Steps¶

Congratulations on completing the IBM Geospatial Studio Workshop! Here's how to continue your journey with geospatial AI.

🎯 Immediate Next Steps¶

1. Apply to Your Use Case¶

Now that you understand the platform, try it with your own data:

Identify your use case: - What geospatial problem do you want to solve? - What data do you have or can access? - What output do you need?

Common applications: - Disaster response: Flood mapping, fire detection, damage assessment - Agriculture: Crop monitoring, yield prediction, irrigation planning - Environmental monitoring: Deforestation, land cover change, water quality - Urban planning: Building detection, infrastructure mapping, growth analysis - Climate science: Temperature mapping, snow cover, vegetation health

Get started: 1. Prepare your training data (if fine-tuning) 2. Onboard data to Studio 3. Try existing fine-tuned models first 4. Fine-tune foundation models if needed for better accuracy 5. Run inference on your area of interest

2. Explore Fine-tuned Models¶

Test existing fine-tuned models in the catalog before training your own:

from geostudio import Client

client = Client(geostudio_config_file=".geostudio_config_file")

# List available models
models = client.list_models(output="df")
print(models[['name', 'description', 'task_type']])

# Try a model on your area
request = {
    "model_display_name": "prithvi-eo-flood",
    "description": "Test on my area",
    "location": "Your location",
    "spatial_domain": {
        "bbox": [[min_lon, min_lat, max_lon, max_lat]]
    },
    "temporal_domain": ["2024-01-01_2024-01-02"]
}

response = client.submit_inference(data=request)

3. Experiment with Different Models¶

Compare model performance:

Try different foundation models: - Prithvi EO V1 (100M) - Faster, less memory - Prithvi EO V2 (300M) - Better accuracy, more resources - Clay V1 - Alternative architecture

Test different configurations: - Various learning rates - Different batch sizes - Multiple training epochs - Different optimizers

Use MLflow to compare: - Access MLflow UI at http://localhost:5000 - Compare metrics across experiments - Visualize training curves - Select best performing model

📚 Deepen Your Knowledge¶

Learn More About Geospatial AI¶

Foundation models: - Read the Prithvi paper - Explore Terratorch documentation - Study model architectures

Geospatial concepts: - Remote sensing fundamentals - Satellite data processing - Coordinate reference systems - Spectral indices (NDVI, NDWI, etc.)

Machine learning: - Transfer learning principles - Computer vision techniques - Model evaluation metrics - Hyperparameter optimization

Recommended Courses¶

Free online courses: - Coursera - GIS Specialization - Fast.ai - Practical Deep Learning - Google Earth Engine

Books: - "Python for Geospatial Data Analysis" by Bonny P. McClain - "Deep Learning for the Earth Sciences" by Gustau Camps-Valls et al. - "Remote Sensing and Image Interpretation" by Lillesand et al.

Join the Community¶

Connect with others: - OSGeo Community - Open source geospatial - Hugging Face Geospatial - Model discussions - GitHub Discussions - Ask questions

Contribute: - Report bugs and issues - Suggest features - Submit pull requests - Share your use cases - Write tutorials

🚀 Advanced Topics¶

1. Custom Model Development¶

Create custom architectures: - Modify existing models - Add custom layers - Implement new loss functions - Create task-specific heads

Resources: - Terratorch Custom Models - PyTorch Lightning - Model Zoo

2. Advanced Data Processing¶

Preprocessing pipelines: - Cloud masking - Atmospheric correction - Pansharpening - Data fusion

Augmentation techniques: - Geometric transformations - Spectral augmentation - Temporal augmentation - Synthetic data generation

Tools: - GDAL - Geospatial data processing - Rasterio - Python raster I/O - Xarray - Multi-dimensional arrays

3. Production Deployment¶

Scale your deployment:

Infrastructure: - Kubernetes orchestration - Auto-scaling policies - Load balancing - High availability setup

Optimization: - Model quantization - ONNX conversion - TensorRT acceleration - Batch processing

Monitoring: - Performance metrics - Resource utilization - Error tracking - Cost optimization

Resources: - Kubernetes Deployment Guide - MLOps Best Practices - Model Serving

4. Integration Patterns¶

API integration:

# Automated pipeline example
import schedule
import time

def daily_inference():
    """Run inference daily for monitoring"""
    client = Client(geostudio_config_file=".geostudio_config_file")

    # Define area of interest
    request = {
        "model_display_name": "prithvi-eo-flood",
        "description": "Daily monitoring",
        "location": "Monitoring area",
        "spatial_domain": {"bbox": [[...]]},
        "temporal_domain": [f"{today}_{ today}"]
    }

    # Submit and monitor
    response = client.submit_inference(data=request)
    client.poll_inference_until_finished(response['id'])

    # Process results
    process_results(response['id'])

# Schedule daily at 2 AM
schedule.every().day.at("02:00").do(daily_inference)

while True:
    schedule.run_pending()
    time.sleep(60)

Webhook integration: - Trigger workflows on completion - Send notifications - Update dashboards - Archive results

Data pipelines: - Automated data ingestion - Continuous model updates - Result distribution - Quality assurance

🎓 Certification & Recognition¶

Document your project: - Write a blog post - Create a tutorial - Present at meetups - Publish a paper

Showcase examples: - GitHub repository - Hugging Face Space - Interactive demo - Video walkthrough

Get recognized: - IBM Champion program - Open source contributions - Conference presentations - Academic publications

🔬 Research Opportunities¶

Explore Research Topics¶

Active research areas: - Multi-modal learning (SAR + optical) - Few-shot learning for rare events - Self-supervised pre-training - Uncertainty quantification - Explainable AI for geospatial

Collaboration opportunities: - IBM Research partnerships - Academic collaborations - Open source projects - Hackathons and challenges

Datasets for Research¶

Public datasets: - HLS Burn Scars - HLS Flood - GeoBench - SpaceNet - xView

Create your own: - Label your data - Share with community - Publish on Hugging Face - Contribute to benchmarks

💼 Career Development¶

Build Your Portfolio¶

Project ideas: 1. Disaster monitoring system - Real-time flood/fire detection 2. Agricultural dashboard - Crop health monitoring 3. Urban growth tracker - Building detection over time 4. Environmental monitor - Deforestation alerts 5. Climate analyzer - Temperature trend analysis

Skills to develop: - Geospatial data processing - Deep learning model training - API development - Cloud deployment - Data visualization

Job Opportunities¶

Roles using these skills: - Geospatial Data Scientist - Remote Sensing Engineer - ML Engineer (Geospatial) - GIS Developer - Earth Observation Analyst

Industries: - Technology companies - Government agencies - Environmental organizations - Agriculture tech - Insurance and risk assessment

🌟 Success Stories¶

Learn from Others¶

Example applications: - Disaster response: Rapid flood mapping for emergency services - Agriculture: Crop yield prediction for farmers - Conservation: Wildlife habitat monitoring - Urban planning: Infrastructure development tracking - Climate research: Long-term environmental change analysis

Case studies: - IBM Research Blog - Hugging Face Model Cards - Academic Papers

📅 Stay Connected¶

Regular Activities¶

Weekly: - Check GitHub for updates - Try new features - Experiment with models - Read documentation updates

Monthly: - Review new papers - Attend webinars - Participate in discussions - Share your progress

Quarterly: - Evaluate your projects - Update your skills - Contribute to community - Plan next steps

Resources to Follow¶

Blogs & News: - IBM Research Blog - Towards Data Science - GIS Lounge

Social Media: - Follow @IBMResearch - Join LinkedIn groups - Participate in Reddit communities

Newsletters: - IBM Research updates - Geospatial AI news - Open source announcements

🎁 Additional Resources¶

Quick Reference¶

Essential links: - Geospatial Studio Docs - SDK Documentation - Terratorch Docs - GitHub Repository

Support: - Troubleshooting Guide - FAQ - GitHub Issues - Additional Resources

Workshop Materials¶

Download: - Workshop Notebooks - Example Datasets - Presentation Slides

Reference: - Lab 1: Getting Started with IBM Geospatial Studio - Lab 2: Onboarding Pre-computed Examples - Lab 3: Upload Model Checkpoints and Run Inference - Lab 4: Training a Custom Model for Wildfire Burn Scar Detection

🚀 Your Journey Continues¶

You've completed the workshop, but this is just the beginning! The geospatial AI field is rapidly evolving, and there are endless opportunities to learn, create, and contribute.

Remember: - Start small, iterate quickly - Share your learnings - Ask for help when needed - Contribute back to the community - Have fun exploring!

We'd love to hear about your projects! - Share on GitHub - Post on social media - Write a blog post - Present at meetups

Thank you for participating in this workshop! 🎉

Questions or feedback? - Open an issue on GitHub - Join the discussion on GitHub Discussions - Contact IBM Research

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