Geospatial / Delivered project

Global Energy Infrastructure Detection

A computer-vision pipeline for identifying physical assets in aerial imagery and delivering traceable, GIS-ready datasets.

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Coastal city and energy infrastructure analyzed by geospatial computer vision — Geospatial · Data → Intelligence → ActionPhoto by Kay Mayer · Unsplash License ↗

01 / Problem statementDelivered project · Geospatial

The operating problem

Mapping roads, utilities, facilities, and other physical assets across large areas requires slow manual interpretation and repeated verification.

Conditions the system must survive03 operating constraints

Imagery resolution, season, geography, and sensor characteristics vary

Assets are sparse and visually similar to background structures

Outputs require geographic accuracy and traceable review

02 / How we approached it

From operating uncertainty to testable evidence.

The work was decomposed into four engineering decisions. Each one produced an artifact the customer could inspect, test, and carry into deployment.

01Frame

Define asset taxonomy and geographic coverage

Evidence produced

Asset taxonomy, coverage, and imagery suitability study

02Evaluate

Curate multi-source aerial and satellite imagery

Evidence produced

Multi-region detection and segmentation benchmark

03Engineer

Detect and segment assets with confidence estimates

Evidence produced

Geographic error and confidence-calibration review

04Operationalize

Validate outputs and publish versioned GIS layers

Evidence produced

Versioned GIS publication pipeline with analyst queues

03 / Deployment record

Deployed around the workflow—not beside it.

The system boundary includes where inference runs, how evidence reaches existing tools, and how people handle uncertainty after launch.

Implementation statusDelivered project

Topology

Scalable cloud imagery pipeline with tiled inference, geospatial post-processing, and versioned datasets.

Integration

Satellite and aerial imagery, boundaries, asset catalogs, and GIS tools connect through georeferenced data contracts.

Operation

Low-confidence tiles enter analyst review; imagery and model versions produce reproducible GIS layer releases.

04 / Evaluation metrics

What must be measured before the system earns trust.

Evaluation covers model behavior, workflow burden, and production performance. The metric defines the gate; the customer baseline and acceptance threshold define the target.

01Evaluation gate

Detection AP and IoU

How it is measured

Class-specific detection precision and segmentation overlap.

What it decides

Defines which assets can enter automated versus reviewed mapping.

02Evaluation gate

Positional accuracy

How it is measured

Geographic distance between predicted and verified asset geometry.

What it decides

Determines GIS fitness for the intended use.

03Evaluation gate

Analyst review rate

How it is measured

Share of detections requiring correction or rejection.

What it decides

Forecasts human effort and confidence thresholds.

04Evaluation gate

Area throughput

How it is measured

Square kilometers processed per compute hour and release cycle.

What it decides

Validates mapping economics at target coverage.

05 / Customer perspective

Value has to appear in the customer’s operating day.

What matters in practice

A useful detection includes geometry, confidence, imagery source, acquisition date, model version, and review status.

01Observable value signalFaster mapping across large areas

02Observable value signalConsistent asset taxonomy

03Observable value signalReview queues focused on uncertain detections

04Observable value signalVersioned geospatial evidence

06 / Technology context

Tools follow the system—not the other way around.

Final architecture depends on data quality, operating conditions, integrations, risk, and evaluation criteria established during discovery.

Computer visionSatellite imagerySegmentationGISMLOps

Test the operating assumption

Define the evidence required to move from possibility to production.

Discuss this use case ↗

Global Energy Infrastructure Detection

Mapping roads, utilities, facilities, and other physical assets across large areas requires slow manual interpretation and repeated verification.

From operating uncertainty to testable evidence.

Define asset taxonomy and geographic coverage

Curate multi-source aerial and satellite imagery

Detect and segment assets with confidence estimates

Validate outputs and publish versioned GIS layers

Deployed around the workflow—not beside it.

What must be measured before the system earns trust.

Detection AP and IoU

Positional accuracy

Analyst review rate

Area throughput

Value has to appear in the customer’s operating day.

Tools follow the system—not the other way around.

Related operational systems.

Edge AI for predictive maintenance and leak detection

Computer vision for visual quality inspection

Define the evidence required to move from possibility to production.