AutoGS

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Inspiration

The growing need for democratizing Earth observation data analysis inspired AutoGS, combining AI chatbots and automated workflows to make satellite insights accessible to non-experts while handling complex raster data challenges.

What it does

AutoGS provides a natural language interface for querying satellite data, automating preprocessing/analysis via AWS services, and generating real-time geospatial insights like disaster impact maps or agricultural forecasts.

How we built it

We integrated AWS Ground Station for data ingestion, SageMaker for AI models, Lex/Kendra for conversational AI, and serverless architectures (Lambda/Step Functions) to create an event-driven pipeline for raster processing.

Challenges we ran into

1. Handling petabyte-scale satellite data with low-latency requirements
   
2. Training domain-specific NLP models for geospatial queries
   
3. Optimizing GPU-based atmospheric correction for cost efficiency
   
4. Ensuring STAC metadata compliance across hybrid cloud/edge deployments
   

Accomplishments that we're proud of

✅ Reduced analysis time from hours to under 90 seconds per query
✅ Achieved 95% accuracy in automated land cover classification
✅ Developed patent-pending AI workflow for SAR data interpretation
✅ Implemented bias detection for agricultural yield predictions

What we learned

1. Serverless architectures require careful cold-start mitigation for raster processing
   
2. Foundation models need domain-specific fine-tuning for EO terminology
   
3. Hybrid edge-cloud deployments improve disaster response latency
   
4. STAC metadata standardization enables cross-agency collaboration
   

What's next for AutoGS

1. Integration with lunar/Martian observation data for space agencies
   
2. Federated learning implementation for privacy-preserving analysis
   
3. Quantum computing experiments for climate modeling
   
4. Expanded multilingual support for global agricultural communities
   

Built With

• groundstation
• python
• sagemaker