Geospatial AI Engineering

ArcGIS, Earth Engine, RAG/GraphRAG for Spatial Intelligence

Overview

Geospatial AI engineers delivering end-to-end spatial intelligence—from satellite segmentation and hydrography to market and subsurface suitability analysis. We combine ArcGIS/QGIS + PostGIS and Google Earth Engine with Python (GeoPandas, rasterio) and web visualization to produce defensible, decision-ready maps, dashboards, and simulations.

Workflows are production-grade (FastAPI, MongoDB tiles, caching), explainable (RAG/GraphRAG + concise agentic summaries), and cloud-native (AKS/GKE, CI/CD) so insights arrive quickly and reliably.

Key Deliverables

Technical Excellence

Approach & Methodology

  • Data sourcing: Tiles/rasters + vector layers; harmonization and CRS handling
  • Feature engineering: Zonal stats, isochrones, proximity, viewshed, hotspotting
  • Retrieval: Spatial joins for RAG; region-aware context windows in agent prompts
  • Outputs: Map layers, KPIs by region, ranked opportunity/risk lists

Technology Stack

  • GIS Software: ArcGIS Pro/Online, QGIS, Google Earth Engine
  • Spatial Libraries: PostGIS (PostgreSQL), GDAL/rasterio/pyproj, GeoPandas
  • Backend: Python, FastAPI, Pydantic, AsyncIO; Streamlit/Dash for analyst apps
  • Data Storage: MongoDB for time-series/tiles; embeddings & graph stores for relationships
  • Infrastructure: Docker, managed Kubernetes, CI/CD, observability

Measurable Impact

Expected Results

  • Clear "where/why" stories for planners and operators
  • Reusable spatial tiles/pipelines for faster repeat analyses
  • Production hygiene: autoscaling, probes, and cost controls

KPIs We Track

  • Time-to-map
  • p95 latency for spatial endpoints
  • Accuracy of spatial overlays vs. ground truth (when available)
  • Cost per 1k tile requests
  • Cache effectiveness

How We Work Together

Discovery → Build/Integrate → Harden → Operate

Specialized services include:

  • Hydrography workflows: Watershed delineation, stream network extraction, floodplain indicators
  • Satellite imagery: Segmentation/classification (e.g., U-Net/DeepLab), land-cover/NDVI, change detection
  • Suitability analysis: Multi-criteria evaluation (MCE/AHP), terrain/hydro-geology overlays, groundwater potential
  • Market analysis: Trade areas/catchments, gravity models, site scoring and demand heatmaps

Ready to Get Started?

Let's discuss how Geospatial AI Engineering can transform your business