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

Jaroona delivers not just image analysis, but an industrial AI layer for inspection, assessment, and prioritization.
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

AI does not replace technical assessment. It supports pre-analysis, prioritization, and documentation. The professional evaluation remains safeguarded within the human-in-the-loop process.
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

Jaroona turns drone data into industrial decision intelligence — precise, integrable, and scalable.
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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