Scaling AI-Ready Earth Observation Data Pipelines

Most geospatial AI projects don’t fail because of the model; they fail because the data isn’t “AI-Ready.” Earth Observation (EO) data is notoriously messy—fragmented across different coordinate systems, obscured by clouds, and trapped in massive, unstructured files. Typically, data scientists spend 80% of their time on the “muck” of data engineering—cleaning, mosaicking, and tiling before a single inference process can even run.

RasterFlow changes that. Built to bridge the gap between the physical world and AI, RasterFlow automates the heavy lifting of geospatial data curation. It transforms raw satellite and aerial imagery into high-performance, AI-Ready datasets that are optimized for distributed inference at a planetary scale.

In this “Getting Started” session, we will demonstrate how to bypass the infrastructure headaches and go from raw imagery to actionable change detection in minutes.

What you will learn:

  • The Blueprint for AI-Readiness: How to automate the creation of clean, cloud-free mosaics and seamless tensors for model consumption.
  • Planetary-Scale Inference: Leveraging Wherobots’ distributed engine to run SOTA models across massive AOIs (Areas of Interest) without managing GPU clusters.
  • Closing the Loop: Transforming model predictions back into queryable vector data (via Apache Iceberg) for immediate business intelligence.
Hands-On Change Detection Case Studies:
  • Canopy Height Evolution (NAIP Imagery): We will demonstrate how to make multi-year NAIP imagery “AI-Ready” to track vertical vegetation shifts in the New York and Mountain West regions. Using the Meta/WRI Canopy Height Model, we’ll show how to detect carbon stock changes and biomass growth through automated temporal analysis.
  • Dynamic Field Boundaries (PRUE Model): Learn how the Taylor Geospatial Engine (TGE) uses RasterFlow to operationalize the PRUE model. We’ll walk through the process of preparing seasonal Sentinel-2 data to detect shifts in agricultural boundaries, showing how “AI-Ready” data allows for precise monitoring of land use and food security at scale.
Why it Matters:

“AI-Ready” means your data is formatted, aligned, and optimized for the machine. Whether you are using PyTorch, TensorFlow, or pre-trained models from the Wherobots Model Registry, RasterFlow ensures your pipeline is built for production, not just a proof-of-concept.