GeoAI & Earth Observation Modeling

Overview

I help teams turn Earth observation and field data into clear map products and reproducible modeling workflows. The focus is practical delivery: inputs, validation, and outputs that your team can rerun and defend.

Ideal collaborators

Research labs, agencies, and product teams working on agriculture and environmental monitoring, especially when you need spatial products that connect field measurements with satellite covariates.

What you get

• Preprocessed optical satellite stacks with masks and temporal composites when needed
• Feature layers from spectral indices and ancillary data such as terrain and climate grids
• Model benchmarking for spatial prediction using established machine learning baselines and deep learning only when justified
• Validation plan plus error diagnostics and uncertainty summaries suitable for reporting
• Delivered map rasters and vectors ready for GIS and dashboards, with clear provenance notes

Inputs to start

• Area of interest boundary and the time window you care about
• Target variable definition plus any ground truth points, plots, or samples
• Preferred spatial resolution and the level of interpretability you need
• What decision the map should support, for example scouting, reporting, or sampling design

Workflow

1. Scoping call to confirm the decision need, target variable, and available data
2. Data preparation and feature generation with documented assumptions
3. Modeling and validation with transparent comparisons and diagnostics
4. Delivery of maps, a short methods brief, and runnable notebooks or scripts

Typical outcomes

• Map layers at an agreed resolution with legends and metadata
• Validation plots and a concise performance summary tied to your objective
• A reproducible package that supports a paper, report, or internal product handoff