GeoAI and AI GIS Analytics for Predictive Maintenance AI IoT: Drone Inspection of Electrical Distribution Networks
Introduction: The Challenge of Efficient Wide-Area Inspection
Utilities required efficient wide-area inspection that reduces costly manual patrols, prioritises critical defects, and seamlessly integrates with asset registers and work-order systems. This demanded an automated smart infrastructure solution capable of detecting subtle faults on medium-voltage overhead networks, driving a modern approach to IoT product engineering.
Solution Overview: GeoAI-Powered Drone Workflow
EurthTech delivered a combined drone UAS mapping workflow and GeoAI pipeline to automate the inspection of distribution lines and assets. The system uses high-resolution RGB and zoom payloads to capture imagery, which is processed by AI-powered embedded systems in the cloud to detect visual anomalies such as cracked insulators, broken conductors, and vegetation encroachment. This workflow drives a proactive Predictive maintenance AI IoT strategy.
Technical Implementation: AI GIS Analytics and AI Engineering Solutions
The processing pipeline stitches images into orthomosaics and associates them with existing GIS asset geometries. We deployed two model families: object-level detection models (YOLO/RetinaNet variants) for physical defects, and segmentation models (U-Net variants) for vegetation encroachment. AI GIS analytics assign a severity score to each defect based on contextual features (asset criticality, historical failure rate), which is key to prioritizing work. This entire process showcases our deep AI engineering solutions expertise.
Results & Impact: Geospatial Engineering and Smart City Solutions
On pilot corridors (~120 km), the automated workflow reduced manual patrols by ~62%. More critically, the risk-based, prioritized dispatch reduced outage events attributed to vegetation by ~35% in the first six months. This high-ROI outcome confirms our strength as a smart city solutions provider delivering scalable geospatial engineering services for critical utilities.
Scalability & Embedded Context: The system integrates defects directly into the asset management system as pre-filled work-orders. Reliable mapping requires careful payload calibration and strict flight SOPs. The data forms a continuous feed for the utility's Digital twin smart city model, allowing planners to use a risk-based algorithm that combines severity and supply-impact scores for optimal maintenance scheduling, a service often provided by a specialized GIS consulting company.