GIS & Geospatial Services

GeoAI/ML Engineer

Manually digitizing land cover, buildings, or infrastructure from satellite and aerial imagery doesn't scale past a handful of tiles, and clients with country- or region-scale imagery need automated extraction that still holds up to scrutiny. This service builds and trains models for feature extraction, object detection, and land-cover classification directly on your imagery, calibrated against your accuracy requirements rather than a generic benchmark. It solves the real bottleneck in most remote-sensing programs: turning petabytes of imagery into structured, usable vector data without an army of manual digitizers. We validate model outputs against ground truth before anything is treated as production data.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

1
Diagnose imagery sources, resolution, and target feature classes to define an achievable accuracy target
2
Pilot a model on a labeled sample tile set and benchmark precision and recall against ground truth
3
Review misclassifications with domain experts and retrain on the specific failure cases found
4
Build the production inference pipeline and operate it with ongoing accuracy monitoring on new imagery

What You Get

Deliverables from this engagement

Trained feature-extraction or classification model
Validated vector outputs (buildings, land cover, or target objects) from source imagery
Accuracy and confusion-matrix report against ground truth
Production inference pipeline for processing new imagery batches

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Imagery Input

Raw Satellite Imagery Tiles
Aerial Imagery Archive
Local Preprocessing Script
Ground Truth Reference Samples

Local Inference Pipeline

Pretrained Detection Model
Land Cover Classifier
Feature Extraction Script
Vector Output Generator

Validation and Delivery

Manual Accuracy Spot Check
Output Shapefile
Client Deliverable Package
Delivery Notes and Accuracy Report

Cloud-Native / Managed Services Architecture

Cloud Imagery Storage

Satellite Provider Feed
Cloud Object Storage
Imagery Tiling Service

Managed ML Compute

Managed GPU Training Job
Object Detection Model
Land Cover Classification Model
Model Registry

Serving and Storage

Inference Endpoint
Cloud Geodatabase
Feature Service API

Downstream Consumers

GIS Analyst Dashboard
Client Applications

Event-Driven & Automation-First Architecture

Imagery Arrival

New Imagery Tile Uploaded
Storage Event Trigger
Inference Job Queue

Automated Inference

Inference Worker
Detection and Classification Model
Post Processing Script
Drift Detection Monitor

Automated Actions

Model Drift Detected
Auto Ingest to Database
Trigger Retraining Job
Notify ML Engineer
Retrained Model Deployment

Enterprise-Grade & Compliance-Ready Architecture

Data Governance

Versioned Training Imagery Set
Ground Truth Label Registry
Access Control Layer

Redundant MLOps Cluster

Primary Training Cluster
Secondary Inference Cluster
Model Validation Gate
Bias and Accuracy Audit

Compliance and Signoff

Audit Trail Store
Compliance Officer Signoff
Model Deployment Approval

Production Deployment

Production Inference Service
Enterprise Geodatabase

AI-Augmented, Human-in-the-Loop Architecture

Imagery and Ground Truth

New Imagery Batch
Existing Ground Truth Samples

AI Detection and Classification

Feature Extraction Model
Object Detection Model
Land Cover Classifier
Confidence Scoring

Human Validation Checkpoint

Confidence Above Threshold
Auto Accepted Detections
GIS Analyst Review
Corrected Labels Returned

Production Data

Approved Vector Dataset
Updated Training Set

Integration-Heavy / Ecosystem-Connected Architecture

GeoAI Engine

Detection and Classification Models
Vector Output Generator

Imagery Providers

Planet Labs API
Sentinel Hub Feed
Client Drone Imagery Feed

Client GIS Ecosystem

ArcGIS Enterprise
PostGIS Database

Operations and BI

BI Dashboard
Ticketing System
Asset Management System
Notification to Field Teams

Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about GeoAI/ML Engineer

Most engagements like this start as a $500–$2,500 pilot, see full pricing.