GeoAI
GeoAI platform engineering
Enterprise and open-source GIS platforms, spatial data pipelines and Web GIS applications engineered end to end.
- • ArcGIS Pro
- • ArcGIS Enterprise
- • ArcGIS Online
- • QGIS
GeoAI
Spatial intelligence for decisions that depend on place
A global Python geospatial practice combining earth observation, GIS engineering and machine learning.
Conceptual visualization only
Capabilities
GIS application development
GeoAI
Earth observation
Remote sensing
Satellite-image analysis
UAV and drone analytics
Digital twins
Smart cities
Web GIS
Spatial databases
Location intelligence
Network analysis
Route optimization
Land-use and land-cover classification
Object detection
Semantic segmentation
Change detection
Flood mapping
Disaster-risk intelligence
Agriculture monitoring
Infrastructure condition assessment
Utility-network management
Environmental monitoring
Urban-growth analysis
Predictive spatial modelling
2D and 3D visualization
BIM–GIS integration
IoT and real-time sensor mapping
Platform and engineering capabilities
Every card opens its own capability page with the full technology stack, specialist agents, workflow, inputs, outputs and live integration status.
Python
Core Python engineering
Production Python: typed data code, packaging, tests, logging and documentation that survives handover.
- • Python
- • NumPy
- • Pandas
- • SciPy
Machine learning
Machine learning and AI engineering
Model development, computer vision and evaluation with an MLOps path to production.
- • scikit-learn
- • PyTorch
- • TensorFlow
- • Keras
APIs and applications
API and application engineering
Typed APIs, authentication, background workers and service architecture you can operate.
- • FastAPI
- • Flask
- • Django
- • REST APIs
Data engineering
Data engineering
Batch and streaming pipelines, warehouse modelling and data-quality enforcement.
- • Apache Spark
- • Dask
- • Airflow
- • Kafka
Databases
Database engineering
Schema design, spatial and vector storage, migrations and performance tuning.
- • PostgreSQL
- • PostGIS
- • SQL Server
- • Oracle
6 matching entries also exist in the published catalog and stay reachable at their original URLs.
How This AI Agent Works
Two spatial processes you can inspect: interactive GIS analysis and earth-observation analysis. Both pause for specialist approval before results are released.
Spatial analysis workflow
From uploaded spatial data to a validated result layer, statistics and exports.
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. Upload spatial data: Input. GeoJSON, shapefile export or feature service reference.
- 2. Validate GeoJSON and CRS: Validation. Check geometry validity, projection and feature count.
- 3. Add to map: Action. Render the source layer on the workspace map.
- 4. Select spatial tool: Decision. Choose buffer, overlay, proximity or zonal statistics.
- 5. Run geoprocessing: Approved tool. Execute the selected operation on the spatial service.
- 6. Validate geometry: Validation. Check topology, area totals and null geometries.
- 7. Generate result layer: AI agent. Assemble the analysis layer and its statistics.
- 8. Specialist review: Human approval. A GIS specialist approves before results are released. Human approval required.
- 9. Map, statistics and export: Output. Interactive map, statistics table and GeoJSON download.
Approval gate: Specialist review. 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 |
|---|---|---|---|---|---|---|---|
| gis-analysis-agent | 9 | Yes. Policy: always | Map | Available | Available (signed in) | Available via Studio · requires ai_provider, python_service | Verified at 390px |
| earth-observation-workflow | 8 | Yes. Policy: always | Map | Available | Available (signed in) | Available via Studio · requires ai_provider, python_service, earth_engine | Verified at 390px |
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.
Supported geospatial technologies
- ArcGIS Pro
- ArcGIS Enterprise
- ArcGIS Online
- QGIS
- GeoServer
- PostGIS
- Google Earth Engine
- Mapbox
- Leaflet
- OpenLayers
- CesiumJS
- GeoPandas
- GDAL
- Rasterio
- Shapely
- Fiona
- PyProj
- xarray
- rioxarray
- Dask
- OSMnx
- NetworkX
- Folium
- Kepler.gl
- Deck.gl
- STAC
- Cloud-Optimized GeoTIFF
- GeoParquet
What a GeoAI engagement looks like
Conceptual visualization only
Spatial analytics, agent-run and human-approved
An illustration of the layered outputs a GeoAI run produces. Real charts, maps and statistics are generated in the workspace from your own data.
Run a GIS analysis now — no sign-in
A real server-side run against a bundled synthetic dataset: persisted run steps, a genuine geodesic buffer, live statistics and GeoJSON/PDF downloads. Labelled Demonstration Mode and excluded from production metrics.
Public GIS demonstration — geodesic buffer
No sign-in required. Ravi corridor monitoring sample (synthetic). Runs are rate limited and stored separately from production metrics.
Demonstration Mode
The demonstration above executes real geometry operations and stores every step in the database, so an output stays retrievable by its run ID. Private uploads, saved run history and live provider models remain in the authenticated GeoAI workspace.
Basemap explorer
Sample basemap extents for orientation only.
Sample data
Layer controls — configuration requiredDraw and measure — configuration requiredUpload GeoJSON — configuration requiredFeature search — configuration required2D / 3D switch — configuration required
Ready to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.