Designing Scalable Data Pipelines for Earth Observation in the Cloud
Updated on March 31, 2026 19 minutes read
Frequently Asked Questions
Do I need deep domain expertise before building Earth observation pipelines?
Not at the start, but you do need enough domain context to define meaningful labels, evaluation windows, and operational outcomes. The best projects usually pair engineering strength with a domain expert who can tell you whether a feature reflects real environmental signal or just seasonal noise.
Should I store raster pixels directly in Parquet?
Usually not. Parquet is excellent for metadata, features, and structured labels, but raw raster and array-heavy workloads are generally better served by COG or Zarr, depending on whether you need scene-centric or cube-centric access.
How do I prevent spatial leakage in model evaluation?
Split data by tile, region, parcel group, watershed, or time block rather than randomly at the row level. In Earth observation, nearby samples often share so much context that random splits can make a weak model look artificially strong.
When does privacy become a real issue in geospatial projects?
It becomes serious as soon as open imagery is linked to identifiable farms, households, properties, inspections, or service usage. At that point, governance, access control, retention policies, and legal review should be part of the pipeline design rather than an afterthought.