I started by trying to break down the surface area of the Earth, then guessed at image resolution and coverage overlap.
Break down the problem into estimating the total volume of imagery data (in petabytes) and then applying cloud storage costs per GB/month. Use a top-down approach: estimate the number of high-resolution photos, average file size, and total storage needed, then multiply by Google Cloud storage rates. State assumptions clearly and consider compression and tiered storage.
Pro tip: Differentiate between raw imagery and processed tiles; Google Earth likely stores both, but the majority of cost comes from the raw data. Also, mention that Google uses its own infrastructure, so costs are internal and lower than public cloud rates.
Define what 'Google Earth's photos' includes: satellite imagery, aerial photos, Street View? Assume we're estimating storage cost for all imagery data. State assumptions about resolution, coverage, and update frequency.
Calculate the number of images and average file size. For example, Earth's land area ~150 million km²; at 1m resolution, each km² needs ~1 million pixels; with 3 bytes per pixel (RGB), that's ~3 MB per km² uncompressed. Multiply by area and consider multiple layers/zooms.
Convert total data to GB or TB. Use a cost per GB/month for cloud storage (e.g., Google Cloud Storage ~$0.02/GB/month for standard). Multiply to get monthly cost, then annualize.
Account for compression (e.g., JPEG reduces size by 10x), redundancy (replication factor 3), and tiered storage (cold data cheaper). Also consider that Google uses custom infrastructure, so cost may be lower.
Compare with known data points (e.g., Google Earth has petabytes of data). Present a range (e.g., $10M-$100M/year) and highlight key drivers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.