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It is important for the electricity transmission and distribution (T&D) companies to patrol their own assets frequently in a wide area. however, the cost of patrolling throughout the area is budget threatening. The work on detecting the maintenance places where the vegetation encroachment problems occurred, is labor intensive, costly, and time-consuming, sometimes inapplicable due to the poor accessibility, and is thus, only practical on relatively small areas. Satellite imagery-based monitoring is reasonable and repeatable; hence it has a potential to replace the helicopter surveillance. Sentinel-2 imagery is one of the most famous satellite imageries with completely free of charge, however, its spatial resolution is relatively lower than high-cost satellite imagery such as PlanetScope or WorldView-3. In this research, we explored the effectiveness of super resolution. The refinement of spatial resolution from 10m/pix to 3.3m/pix (x3 SR) seemed to be extremely useful to assess trigonometric risk assessment, which leveraged the number of the pixels between transmission line and vegetation, and tree height information at the vegetation pixels. We employed the deep learning based super resolution model RDN (Residual Dense Network) to upsample the Sentinel-2 images. The training data is generated from the PlanetScope imagery whose resolution is 3.7m/pix. Deep learning based super resolution is generally effective to get 2-4 times finer resolution, therefore, the PlanetScope imagery is suitable to obtain the RDN model for x3 super resolution. We evaluated the performance of vegetation segmentation performance with and without super resolution in the areas along the transmission line. The experimental results showed that the imagery with super resolution yielded better result than the result without super resolution by 9.3% in weighted F1-score.
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