Hi! I'm KG, and until recently, I'd never participated in a Kaggle challenge, let alone automated anything significant using GitHub Actions. But curiosity—and a fluffy Shiba Inu named Fuwa and a Poodle named Coco—guided me down an intriguing path.
Initially, analyzing satellite imagery for undiscovered archaeological sites seemed daunting. But inspired by Fuwa’s silent encouragement during a quiet walk in the park, I decided to experiment with AI-driven analysis, specifically using NDVI (Normalized Difference Vegetation Index) data.
The Journey to Automation
Here's how I went from manual analysis to a fully automated AI pipeline:
Step 1: Manual Exploration – I started by manually analyzing NDVI and satellite imagery using Google Earth Engine. Although insightful, this process was labor-intensive and unsustainable.
Step 2: Introducing AI (GPT-4) – To enhance my efficiency, I integrated GPT-4 into the workflow. GPT-4 helped interpret anomalies, generate hypotheses, and structure my analysis, significantly reducing manual effort.
Step 3: Creating a Python Pipeline – I wrote Python scripts (
run_analysis.py,report_generator.py) to automate NDVI analysis and report generation, allowing me to systematically process multiple regions and consistently document findings.Step 4: GitHub Actions for Automation – Finally, I integrated my pipeline into GitHub Actions. This allowed fully automated execution: fetching data, analyzing anomalies, generating detailed markdown reports, and visualizing results—all triggered automatically upon code updates or scheduled intervals.
Challenges & Learning
Throughout this journey, I faced numerous challenges:
Version Control Conflicts: Managing Git conflicts was initially confusing, but resolving these taught me valuable lessons about maintaining clear repository structures and careful collaboration.
Automated Dependency Management: Errors such as missing or incorrect dependencies (
requirements.txt) were frustrating but essential learning moments in creating robust, repeatable setups.Debugging Automation Failures: Issues like "No such file" or pipeline script errors taught me resilience and systematic debugging techniques.
Result: Fully Automated Archaeological Insights
Today, my pipeline runs smoothly:
Automatically detects vegetation anomalies using satellite imagery.
Produces clear, detailed markdown reports ready for Kaggle submission.
Frees me to focus on strategy, interpretation, and further experimentation.
This automation transformed my exploration, allowing deeper insights, faster iteration, and sharing discoveries openly and efficiently.
Why Share This?
I believe everyone should have access to clear examples of automation journeys—from absolute beginner to fully automated workflows. If my story helps or inspires even one person to explore automation and AI-driven research, sharing this is worth it.
Thank you for supporting my exploration and journey. Any contributions go directly towards further refining this pipeline and sharing more automated insights!
– KG & Fuwa Coco🐕🐩