Over the past years, Unmanned Aerial Vehicles (UAVs) have become essential tools for inspection and monitoring tasks in various sectors due to their versatility, low operational cost, and ability to carry a wide range of sensors with which they can be equipped. This paper presents an integrated methodology that combines two complementary approaches using UAV-based imagery and deep learning techniques: the inspection of electrical grid components and the monitoring of urban expansion near power line systems. For infrastructure inspection, object detection models such as YOLOv5 are used to automatically identify electrical assets, enhancing reliability and reducing manual inspection efforts. In parallel, semantic segmentation is applied to orthophotomosaics generated from drone flights to detect and quantify urban growth, allowing the identification of unregistered constructions and potential sources of unauthorized energy use. The proposed dual approach provides actionable geospatial intelligence to support energy utility companies in both maintaining infrastructure integrity and managing irregularities in urban development. Results demonstrate the potential of combining photogrammetry, computer vision, and deep learning for continuous, large-scale monitoring of energy systems and their surrounding environments.