GeneGIS is an AI-native, cloud-native, GPU-native geospatial workbench — not a desktop GIS reimplementation.
The center of GeneGIS is Workflow, not Layer.
Traditional: Data → Layer → Manual Operation → Map
GeneGIS: Intent → Discovery → Workflow Graph → Verified Execution → Map
Apps (Desktop / Browser / Server / CLI)
└── UX (Canvas, Command Palette, AI Chat, Workflow Graph)
└── AI Native Layer (Intent, Planner, Verifier, Provenance)
└── GIS Kernel
├── Core (project, layers, commands)
├── Spatial / Vector / Raster / PointCloud / Tile
├── Query (DuckDB, PostGIS)
├── Analysis (workflow DAG)
└── Rendering (wgpu)
└── Data / Storage / Plugin / Collaboration
- Project model, layer graph, CRS metadata, units
- Command bus (UI, AI, CLI, plugins → same path)
- Undo/redo, provenance, permissions
- Workflow graph schema
Implemented in crates/genegis-core and crates/genegis-workflow.
| Rule | Meaning |
|---|---|
| Original data is immutable | Sources are referenced, not silently mutated |
| Derived data is reproducible | Outputs trace back to workflow graphs |
| Cache is disposable | Rebuild from workflow + source |
| Provenance is append-only | Audit trail for AI and humans |
- Vector: GeoParquet
- Raster: COG (Cloud Optimized GeoTIFF)
- Point cloud: COPC
- Tiles: PMTiles, MBTiles, MVT
- Catalog: STAC, OGC API
AI generates GeoWorkflow IR first — not raw code. Every step carries:
- Operation name and parameters
- Expected schema and validation rules
- CRS / unit requirements
- Citations and review status
See ai-native.md.
wgpu-based pipeline targeting 60fps interactive scenes with:
- Progressive loading for huge datasets
- GPU memory budgets per layer
- LOD for vector, raster tiles, and point clouds
See rendering.md.