GeoAI Academy

Learning Roadmap

7 phases from GIS foundations to production — click any phase for details

  1. 1

    GIS Foundations

    1.5 weeks

    Understanding coordinate systems and spatial data formats

    Every pipeline bug traces back to CRS mistakes — this prevents silent failures

    3 activities · 0 completeView Phase Details
  2. 2

    Remote Sensing Fundamentals

    2 weeks

    Understanding what satellites actually measure and why atmospheric correction matters

    Cuenca has persistent cloud cover — understanding sensor limitations defines the product architecture

    3 activities · 0 completeView Phase Details
  3. 3

    Vegetation Monitoring

    1.5 weeks

    Selecting the right vegetation index for each detection task

    Indices are the features that feed the classifier — wrong index choice = missed detections

    2 activities · 0 completeView Phase Details
  4. 4

    Earth Observation Data Sources

    1.5 weeks

    Building vendor-independent data access pipelines

    GEE has commercial quotas — STAC+COG is the insurance against vendor lock-in

    2 activities · 0 completeView Phase Details
  5. 5

    Geospatial Machine Learning

    4 weeksCritical

    Validating geospatial ML models honestly — the #1 credibility differentiator

    Random train/test splits inflate accuracy by 5-15%. Spatial CV gives honest numbers that survive real-world deployment.

    3 activities · 0 completeView Phase Details
  6. 6

    Deforestation Detection Systems

    2 weeks

    Understanding operational systems to find market gaps

    Most systems are tuned for lowland Amazon — Andean montane forest is underserved

    2 activities · 0 completeView Phase Details
  7. 7

    Production GeoAI Systems

    4 weeksCritical

    Converting a working model into a deployable, scalable platform

    This is where the product gets built — database, API, tile server, architecture

    3 activities · 0 completeView Phase Details