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2026 · Case study
canopy
Detecting urban tree loss and climate risk from geospatial data.
Designed a geospatial AI pipeline for tree-loss detection and intervention planning.
The problem
Cities lose tree cover faster than manual surveys can track — making climate risk hard to quantify and interventions reactive.
Why it's interesting
Applies remote sensing and geospatial ML to an environmental problem with direct civic impact.
What I built
- Tree loss detection from geospatial imagery
- Climate risk prediction layer
- City intervention optimization model
Technical components
PythonGeospatial AIRemote sensing data processingSatellite/aerial imagery analysis
What made it non-trivial
Extracting reliable tree-loss signals from noisy geospatial data across varying urban densities.
My role
ML engineer — remote sensing, geospatial models, analysis pipeline.