Geospatial ML Pipelines

Purpose

Outline how geospatial machine learning systems move from raw remote sensing data to reproducible training, inference, and evaluation workflows.

Outline

• Building training datasets from imagery, labels, vector features, and temporal context
• Managing chips, samples, splits, metadata, and leakage risks in spatial ML
• Feature engineering and preprocessing for raster, vector, and time-series inputs
• Batch inference patterns for large areas and long time ranges
• Evaluation, uncertainty, monitoring, and model versioning for geospatial products

Later Examples

• Creating spatially aware train, validation, and test splits
• Structuring a chip-based land cover classification pipeline
• Running tiled inference and stitching predictions into publishable outputs