Outcomes you get
- Repeatable earth-observation workflows that refresh on a schedule
- Map and dashboard delivery non-specialists can use
- Auditable derivation from source imagery to published layer
Solution
GeoAI & Spatial Intelligence
Earth observation, remote sensing and Web GIS delivered with open Python geospatial stacks.
Conceptual visualization only
The problem we solve
Spatial data sits unused in archives because analysis is manual, slow and impossible to repeat at scale.
What we deliver
- Spatial data assessment and coordinate/quality review
- Imagery ingestion and preprocessing pipeline
- Analysis models (classification, change detection, indices)
- PostGIS data store and published map services
- Web GIS or dashboard interface with export
A good fit if
- Agencies mapping risk, land use or environmental change
- Utilities, mining and agriculture monitoring assets at scale
- Planning teams needing digital-twin style visualisation
How This AI Agent Works
The published process for this agent: what triggers it, what it validates, which approved tools it calls, where a human approves and what it delivers.
GeoAI platform workflow
From a spatial requirement to a published, monitored map service.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
72%
Workflow summary as a text list
- 1. Spatial requirement: Trigger. Area, layers, accuracy and audience.
- 2. Data and CRS validation: Validation. Check geometry validity, projection and completeness.
- 3. Spatial processing: Approved tool. Run the geoprocessing chain on the spatial service.
- 4. Map service: Action. Publish tiles or feature services for the results.
- 5. Web GIS application: Action. Wire the layers into the workspace map application.
- 6. Testing: Validation. Verify rendering, performance and layer accuracy.
- 7. Publish approval: Human approval. An owner approves before the service is exposed. Human approval required.
- 8. Monitor: Monitoring. Track service health, tile latency and usage.
- 9. Map service and application: Output. Published layers, application and method note.
Approval gate: Publish approval. Deliverable: Map.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| geoai-platform-engineering | 9 | Yes. Policy: always | Map | Available | Available (signed in) | Available via Studio · requires ai_provider, python_service | Not yet covered |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Capabilities
Delivered as consulting, build, automation or managed service.
GIS application development
Earth observation
Satellite-image analysis
UAV and drone analytics
Digital twins and smart cities
Web GIS and spatial databases
Route and network optimization
Change detection
Flood and disaster-risk mapping
Agriculture and environment monitoring
How an engagement runs
Phase 1 · 1–2 weeks
Discover
Area of interest, data sources, accuracy targets.
Phase 2 · 1–2 weeks
Design
Pipeline design, model choice, delivery format.
Phase 3 · 4–10 weeks
Build
Processing, validation against ground truth, web delivery.
Phase 4 · Ongoing
Deploy & improve
Scheduled refresh, new indicators, training.
Common questions
Which imagery sources do you use?
Open collections such as Sentinel and Landsat by default, plus commercial or UAV imagery where resolution demands it.
How is accuracy validated?
Against ground-truth or reference datasets, reported with confusion matrices and per-class metrics.
Ready to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.