Field inspections of utility network assets are a critical aspect of maintaining the quality and integrity of services provided by a utility. Currently, such inspections are performed largely by human inspectors. Such manual inspections are time-consuming, expensive, potentially dangerous to the inspectors and the surrounding public, subject to human error, disruptive to neighboring communities, and lack the speedy response needed in a disaster scenario. This disclosure describes a machine-based inspection mechanism for utility networks, based on, for example, autonomous or remotely-operated drones with the capability to perform intricate inspections at difficult-to-reach regions and heights. The inspection tasks including, for example, navigation routes, assets and parameters to be inspected, etc. are determined, for example, by a machine learner that has access to real-time and historical data from a variety of relevant sources.

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