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In Brazil, approximately 30% of unscheduled interruptions of energy supply are caused by fires and vegetation interference in the energy distribution networks, resulting in great losses for companies of the electricity sector. To reduce the interruptions caused by these kinds of events, the energy distribution companies continually monitor and manage the vegetation in the vicinity of electric cables. However, due to the great extension and capillarity of the networks, it is not always possible to cover the entire network, and it is necessary to define priority segments to be managed. Taking into the account this context, the main objective of this study was to develop multi-criteria indicators to identify segments of the energy distribution networks with higher priority for management, based on vegetation attributes extracted from remote sensing images. For this purpose, we tested two artificial intelligence algorithms, support vector machine (SVM) and artificial neural networks (ANN), to automatically identify different classes of vegetation using PlanetScope images. Our results showed that the ANN algorithm presented better results for the vegetation classification when compared to the results obtained with the SVM algorithm. The application of the developed indicators showed adherent results, even in densely urbanized areas. We hope that the use of the developed indicators can help Brazilian energy distribution companies in optimizing vegetation management and consequently reducing unscheduled interruptions. [ABSTRACT FROM AUTHOR]