AI Solutions
DE

Drone AI for Industrial Inspections

We analyze drone, thermal, multispectral, and LiDAR data with computer vision, edge AI, and cloud analytics. The result is structured, georeferenced output for energy facilities, structures, environmental monitoring, and maintenance workflows.

Capture faster. Analyze more precisely. Decide better.

From Drone Imagery to Reliable Decisions

Traditional drone inspections generate large volumes of image material. The real effort begins afterward: reviewing, sorting, assessing, documenting, and prioritizing. We automate exactly this step. AI detects relevant patterns in image and sensor data, precisely geolocates them, and converts them into structured findings. This makes inspection processes faster, more consistent, and easier to compare.

Traditional Inspection

  • Manual review of large image volumes
  • Inconsistent assessment
  • Time-consuming documentation
  • Point-by-point individual checks
  • Images as raw data

AI-Powered Inspection

  • Automated preliminary analysis
  • Standardized classification
  • Automated findings reports
  • Comparison across time series
  • Data as a basis for decisions

Faster Inspections

Of large facilities and infrastructure areas through automated preliminary analysis

Automated Detection

Of damage, anomalies, and risk zones with reproducible classification

Seamless Integration

Into existing maintenance, GIS, and reporting systems without hardware replacement

Human-in-the-Loop

Technical review and approval always remain with specialist personnel

This turns drone flights into more than just images — they become reliable decision foundations for maintenance, safety, operations, and capital planning.

Manufacturer-agnostic. Modular. Integrates into existing systems.

The Jaroona solution is not tied to any specific drone manufacturer. It is integrated as a software and AI layer into existing drone, sensor, and data environments. This allows existing investments to continue being used—complete hardware replacement is generally not required.

Supported data sources

  • 4K and 8K RGB image data
  • Thermal cameras
  • Multispectral sensors (NIR, red edge)
  • Hyperspectral data for specialized applications
  • LiDAR point clouds
  • GPS, RTK, and metadata
  • Time series from plant and operational data

Typical integration targets

  • Drone platforms and flight planning systems
  • Cloud storage and data lakes
  • GIS systems such as ArcGIS or QGIS
  • Maintenance systems such as SAP PM or IBM Maximo
  • Power BI, web dashboards, or custom portals

Technical Architecture of the Jaroona Drone AI

The solution combines modern computer vision models, georeferenced data processing, and industrial integration architecture. Depending on the use case, analysis takes place directly during flight, downstream in the cloud, or in a hybrid architecture.

1

Data Capture

Drones capture RGB, thermal, multispectral, or LiDAR data, including position, altitude, and camera metadata.

2

AI Preprocessing

Image data is normalized, rectified, georeferenced, and prepared for model analysis.

3

Detection & Segmentation

YOLO-based models detect objects and damage patterns. U-Net, Mask R-CNN, and transformer-based approaches delineate damaged areas at pixel level.

4

Classification & Prioritization

Findings are evaluated by type, location, extent, severity, and urgency.

5

Reporting & Integration

Results are delivered as a dashboard, findings map, inspection report, or API export.

Real-time Edge Analysis and In-depth Cloud Evaluation

Not every inspection requires the same processing logic. We flexibly combine edge AI and cloud AI—depending on time sensitivity, data volume, and integration requirements.

Edge analysis during the flight (optional)

For time-critical applications, the AI runs directly on the drone or a mobile edge system. The field team receives indications of anomalous areas during the flight.

  • Initial anomaly detection
  • Hot spot detection
  • Object counting
  • Navigation support for follow-up captures
  • Pre-prioritization of critical areas

Cloud analysis after the flight

For deeper analyses, model comparisons, time series, 3D point clouds, and inspection reports, processing takes place in the cloud backend.

  • Segmentation of large data volumes
  • 3D comparison of multiple inspection flights
  • Automated report generation
  • Integration into CMMS, GIS, and BI systems
  • Model versioning and auditability

Focus Area: Energy Infrastructure

Energy infrastructure is often expansive, difficult to access, and mission-critical. This is exactly where AI-powered drone analysis delivers its greatest value. We support operators with automated inspection across a broad range of assets and deliver a prioritized, traceable, and digitally actionable findings basis.

Power Lines & Substations

High-voltage transmission lines, towers, and substations with automated corrosion and damage analysis

Wind Turbines

Rotor blades, tower structures, and lightning protection zones for surface damage and thermal anomalies

Photovoltaic Parks

Module defects, hot spots, string anomalies, and vegetation risks in large-scale solar installations

Dams & Water Infrastructure

Crack formation, moisture zones, erosion areas, and structural changes in water infrastructure assets

Dams and Water Infrastructure

We combine high-resolution image data, thermal and multispectral analysis with georeferenced 3D models. The key is the early identification of changes that indicate structural risks, moisture ingress, or material fatigue.

Detected Finding Types

  • Cracks and settlement cracks
  • Surface changes
  • Moisture zones and seepage traces
  • Erosion areas
  • Anomalies at construction joints
  • Changes between multiple inspection dates

Technical Approach

  • Segmentation of crack and damage areas
  • Classification by finding type and severity
  • Comparison of historical inspection data
  • Linking with GPS, RTK, and 3D reference data

Solar Parks and Wind Turbines

For operators of solar and wind power plants, the financial impact of inspections is especially easy to measure: defects detected faster, maintenance targeted more precisely, fewer unplanned downtimes, and higher plant availability.

Solar Park Analysis

  • Hot spots and damaged modules
  • Conspicuous module groups and wiring issues
  • String and substring deviations
  • Vegetation and shading risks

Results can be combined with SCADA, weather, and yield data for a more precise picture of the economic relevance of individual findings.

Wind Power Analysis

  • Rotor blades and tower structures
  • Lightning protection areas
  • Surface damage
  • Thermal anomalies
  • Recurring damage patterns

Maintenance teams receive no unstructured image collection, but a prioritized defect list with location, image evidence, severity, and recommended action.

Additional Use Cases: Environment, Vegetation, and Soil

The Jaroona drone AI is not limited to technical infrastructure. The same architecture is adapted for environmental monitoring, vegetation analysis, and geospatial risk assessment—tailored to the data, goals, and quality requirements of each deployment.

Wildlife Monitoring & Species Conservation

RGB and thermal imagery enable wildlife detection, counting, and spatial analysis. Population surveys, protection measures, and repeatable monitoring processes become scalable.

Plant & Vegetation Analysis

Multispectral and hyperspectral data enable analysis of vegetation condition, water stress, nutrient supply, and pest infestation. Vegetation indices such as NDVI are combined with AI classifications.

Soil Conditions & Erosion

Photogrammetry, LiDAR, and elevation models enable analysis of erosion zones, slope instability, surface runoff, and risk areas after heavy rainfall events.

Inspection data that remains verifiable and traceable

For industrial and public-sector clients, a pure AI mark is not enough. Findings must be traceable, reproducible, and technically verifiable. That is why we generate structured inspection data with full context.

Each finding can include:

  • Finding type and severity
  • GPS or RTK coordinates
  • Image crop and original image reference
  • Timestamp as well as sensor and camera information
  • Confidence score of the AI model
  • Assignment to asset, component, or zone
  • Status of expert review
  • Export to maintenance or reporting systems

Three levels of result quality

AI indication

Automatically detected, not yet reviewed

Prioritized finding proposal

Classified and prepared for review

Verified finding

Professionally confirmed and report-ready

Integration into existing operational processes

The value of drone AI is not created by detection alone. What matters is that findings can be transferred directly into operational workflows. We integrate analysis results into existing system landscapes.

Possible target systems

SAP PM

IBM Maximo

ArcGIS / QGIS

Power BI

Azure / AWS

REST API

Operational benefits

  • Less manual rework
  • Faster handoff to maintenance teams
  • More consistent assessment across sites
  • Better traceability for management, authorities, and engineering
  • Scalability across many assets and regions

Typical outputs

  • Prioritized defect list and digital findings map
  • PDF, Word, CSV, JSON, or GeoJSON export
  • 3D point clouds and DEM models
  • Time-series comparison across multiple inspection flights

From Proof of Concept to a Production Inspection Platform

We recommend a structured entry via a clearly defined proof of concept. The goal is not an abstract AI demo, but a robust validation using real data. The path from the first test data to the production platform is divided into six clear phases.

1

1. Use Case Selection

Definition of the specific inspection objective: crack detection, hotspot detection, vegetation risk, or 3D change analysis

2

2. Data Review

Analysis of existing drone, sensor, GIS, and maintenance data for quality and suitability

3

3. Model Development

Training or adaptation of suitable computer vision models to the specific use case

4

4. Validation

Measurement of detection quality, false alarms, finding coverage, and practical usability

5

5. Dashboard & Reporting

Preparation of results for engineering, operations, and management

6

6. Scaling

Integration into existing processes, systems, and recurring inspection cycles

Measurable PoC Criteria

Detection rate of relevant findings

False positive rate

Processing time per inspection flight

Quality of georeferencing

Completeness of finding documentation

Integration capability into existing systems

Measurable Performance. Clear Numbers.

The performance of Jaroona can be expressed in concrete metrics — measured across real project deployments with validated image data and trained models.

70%

Faster

than classic manual pre-analysis in suitable scenarios

95%+

Detection Quality

with trained models and validated image data

100%

Traceable

through original image, coordinate, timestamp, and model version

AI that turns drone data into decisions.

We integrate computer vision, edge AI, and cloud analytics into existing drone systems. This transforms aerial imagery, thermal captures, multispectral data, and LiDAR point clouds into structured findings for infrastructure, energy facilities, environmental monitoring, and maintenance processes.

Vendor-agnostic

Runs on existing drone and sensor systems without hardware replacement

Modular

Use-case-specific AI pipelines tailored to specific inspection goals

Scalable

From a single asset to an enterprise-wide inspection program

Verifiable

Fully traceable findings with image, location, and model evidence

From images come findings

From findings come decisions. From inspections comes scalable operational intelligence.

We help companies transition drone inspections from manual image review to AI-powered, reproducible, and integrable analysis processes. The journey starts with a clearly defined proof of concept with measurable criteria — and scales from there into a productive, industrial inspection process.

Inspect faster

Automated pre-analysis significantly reduces manual effort

Maintain more precisely

Prioritized findings direct resources where they have the greatest impact

Reduce outages

Early detection of risks prevents unplanned downtime

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