A Framework for Integrating Drone Operations into Enterprise GIS Systems for Utility Companies

Abstract

This study presents a comprehensive framework for integrating drone operations into enterprise Geographic Information Systems (GIS) for utility companies to enhance asset management, operational efficiency, and decision-making capabilities. Utility networks, including electricity, water, and gas systems, rely heavily on accurate spatial data for monitoring, maintenance, and planning. However, traditional data collection methods are often slow, costly, and limited in resolution and frequency. The proposed framework introduces a structured approach that connects drone-based data acquisition with enterprise GIS platforms through interoperable architectures and real-time data pipelines. At the data acquisition level, drones equipped with high-resolution imaging, LiDAR, and thermal sensors capture detailed geospatial data across utility assets such as transmission lines, pipelines, and substations. The integration layer enables seamless data ingestion, processing, and storage using cloud computing and edge processing technologies. Advanced analytics tools, including artificial intelligence and machine learning, are incorporated within the GIS environment to support automated feature extraction, anomaly detection, and predictive maintenance modeling. The framework also emphasizes the importance of standardized data formats, metadata management, and system interoperability to ensure compatibility with existing enterprise GIS infrastructures such as ArcGIS and QGIS. Additionally, it addresses operational considerations including workflow automation, user access control, cybersecurity, and regulatory compliance for drone operations. A phased implementation strategy is proposed, beginning with pilot projects and scaling through organizational integration and continuous optimization. The framework highlights key benefits such as improved data accuracy, reduced operational costs, enhanced situational awareness, and faster response to infrastructure issues. Challenges including data volume management, integration complexity, and skill requirements are also examined. By aligning drone technologies with enterprise GIS systems, this framework supports the digital transformation of utility companies and contributes to more resilient, data-driven infrastructure management. Ultimately, the proposed approach provides a scalable and adaptable model for modernizing geospatial operations in the utility sector.

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