Teams can use machine learning with geospatial observations to study patterns and estimate future conditions.

With imagery and historical data, teams can develop models for crop yields, natural hazard patterns and supply chain conditions. The quality of those outputs depends on consistent source, time, geography and processing details.

Model design should account for spatial and temporal leakage. Randomly separating neighbouring pixels or observations from the same period can make validation results look stronger than performance in practice.

Teams also need a reproducible link between each training example and its source data, preprocessing method and label. That record becomes important when the source is revised, the model is retrained or an output is challenged.

Arche Zero prepares and delivers geospatial data while keeping the records needed to understand and reproduce model inputs.

WHAT TO DEFINE

Set the requirements before choosing a source.

  • The decision or task the data must support
  • The geography, period and minimum spatial resolution
  • The update frequency and acceptable delay
  • The source, processing and licence details to retain