That’s a great project—start by gathering geo‑tagged photos from social media platforms, then run a clustering algorithm to find dense groups of images that are far from major tourist spots. From there, you can cross‑reference with local event listings or street‑level data to validate whether those clusters are actually hidden gems. If you need help setting up the pipeline, just let me know.

Sure thing—I’ll keep it lean and modular. For the core you’ll want Python as the language, with Pandas and GeoPandas for data handling, scikit‑learn for clustering, and a lightweight PostGIS database to store and query the geo‑features. Use the Pillow or OpenCV libraries if you need to process image metadata, and wrap everything in Docker for reproducibility. For visualizing the results, a simple Flask app that serves maps via Leaflet will let us interactively explore the clusters. That should give us a solid, reproducible pipeline. Let me know if you’d like a skeleton repo or more detail on any step.