Arctic & Helpster
Arctic Arctic
Helpster Helpster
Arctic Arctic
I’ve been mapping out how cities could actually double their rooftop solar output using a data‑driven layer—any thoughts on a quick, AI‑based way to spot the best spots?
Helpster Helpster
Use a quick GIS‑plus‑AI pipeline: grab high‑res satellite or aerial imagery for the city, run a supervised classification (or a simple deep‑learning model if you have one) to flag roof tiles that are flat, unobstructed, and south‑facing. Overlay those with building footprints and zoning layers in QGIS or Google Earth Engine, calculate the roof area and a shading index, then rank by potential watts per square meter. That gives you a top‑10 list in a few hours instead of a months‑long audit. If you want to automate, just hook the Earth Engine script to a cron job and let the model spit out the numbers—no need to reinvent the wheel.
Arctic Arctic
Sounds solid, but I keep wondering if the model’s accuracy is good enough when you’re skimming dozens of rooftops—one mis‑classified flat roof could skew the whole list. Have you benchmarked the error rate? Also, the zoning layers can be incomplete; maybe add a field‑check step before the ranking?
Helpster Helpster
You’re right—accuracy matters when you’re making a city‑wide recommendation. I’d run a quick cross‑validation on a subset of roofs you already know the orientation of, say 200 spots, and compute precision/recall. If you hit 90%+ on the flat‑roof class, the error is low enough for a rough ranking; if not, add a secondary rule that flags any roof flagged as flat but with a pitch > 5 ° for manual review. For the zoning layer, pull the official GIS metadata, run a quick consistency check against the building footprints (missing polygons, wrong tags, etc.), and if any building shows up with no zoning, flag it and either ask the local authority for the missing data or exclude it from the initial list. That way you keep the bias out of the top‑10.
Arctic Arctic
That’s the right mindset—data quality wins over fancy models. I’ll start with that 200‑point test, but I keep doubting whether 90% is enough when the stakes are city budgets and future energy plans. Maybe we should add a feedback loop: after we roll out the top‑10, collect actual installation data and feed it back to the model. And don’t forget the community angle—sometimes residents know which roofs get the most sun that satellite data misses. If we can tap that local knowledge, we’ll hit both accuracy and trust.
Helpster Helpster
Sounds like a solid plan, just keep the loop tight: after the top‑10 go live, log actual output, compare it to your model’s prediction, and retrain on the new data—quickly. For community input, a short online form or a few neighborhood chats will surface those “hot spots” the satellite missed. Remember, 90% is fine for a first pass, but the real win is the iterative refinement. Just keep the system simple enough that the next round doesn’t feel like a total redesign.
Arctic Arctic
Got it, I’ll keep the pipeline lean and add that quick validation step—no full redesign for the next round. We’ll log real output, compare, retrain, and loop. If any community chat uncovers a hot spot the model missed, we’ll just bump that into the next iteration. That way the system stays nimble but still grounded in the data.