When Google Research publishes on 'pixels to planning' — turning satellite imagery into sustainable planning decisions — the real technical signal is not in the computer vision model. It is in the data platform that must exist before any pixel is processed: petabyte-scale raster ingestion, geospatial partitioning that does not break under analytical load, lineage traceability that satisfies carbon credit auditors, and inference with predictable latency for operational decisions. I have designed data pipelines for financial-grade environments where a single wrong location attribute cost millions in incorrect hedging. The discipline I learned in those environments applies directly here — and that is what I will document.
The Real Problem: Geospatial Data Is Not Just Large Files
Most multispectral satellite image files arrive as Cloud-Optimized GeoTIFF (COG) or HDF5, ranging from 500 MB to 5 GB per scene depending on resolution and band count. Sentinel-2 alone produces roughly 1.6 TB per day globally. When you start ingesting multiple constellations — Sentinel, Landsat, Planet, SAR radar data — the volume grows to tens of terabytes daily before you even compute derived indices like NDVI, NDWI, or land surface temperature.
