# Dependency Policy

GeoIntel deliberately uses technologies matching the GeoAI Engineer profile. Dependencies must support the core goal: geospatial processing, remote sensing, AI inference, QA/QC, and professional frontend visualization.

## Approved backend core

- FastAPI
- Uvicorn
- Pydantic
- SQLAlchemy
- Alembic
- GeoAlchemy2
- psycopg
- python-multipart

## Approved GIS/remote-sensing

- GeoPandas
- Shapely
- PyProj
- Rasterio
- GDAL where available
- Fiona or pyogrio where needed
- NumPy
- OpenCV when needed for image processing

## Docker GIS runtime

The backend Docker image may install the approved `gis` optional dependency
group so the deployed workbench has real raster/vector runtime capability:

- rasterio
- numpy
- pillow
- geopandas
- pyogrio

The Docker image may also install GDAL, GEOS and PROJ system packages required
by those GIS libraries. This does not enable new product behavior by itself; it
only allows existing raster/vector endpoints to run when requested.

AI dependencies remain separate in the `ai` optional dependency group and must
not be installed by the default Docker backend image unless an explicit AI image
or profile is introduced later.

## Approved AI

- PyTorch
- Ultralytics
- Segment Anything only after detection foundation works

## Approved frontend

- React
- TypeScript
- Vite
- MapLibre GL
- Deck.gl
- TanStack Query
- Zustand or React context for local UI state
- Recharts for charts

## Add-dependency rule

Before adding a dependency:

1. Explain why existing dependencies are insufficient.
2. Add it to this document.
3. Add setup notes if it has native/system requirements.
4. Ensure Docker build still works.

## Avoid in V1

- Heavy MLOps platforms.
- User auth frameworks.
- Full workflow orchestration stacks beyond simple queueing.
- Unnecessary UI component mega-libraries.