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- W3206989807 abstract "Vegetation is one of the primary causes of outages in electricity transmission and distribution networks and represents a significant expense in maintaining a power grid. While LiDAR or multi-view images can be used for detecting vegetation along power lines, such technologies are costly and difficult to acquire to cover widespread electricity networks. This paper proposes a framework for 3D mapping of trees along power lines using monocular high-resolution satellite images. Such type of imagery has become nowadays affordable and easy to acquire. Furthermore, single snapshots can cover a large portion of the grid in high revisiting time. We train and test different state-of-the-art models to map the contextual information from images into a height prediction. We validate our proposed satellite-based framework for an electricity distribution network in the western part of Norway using actual LiDAR data." @default.
- W3206989807 created "2021-10-25" @default.
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- W3206989807 date "2021-07-11" @default.
- W3206989807 modified "2023-10-17" @default.
- W3206989807 title "Automated 3D Vegetation Detection Along Power Lines using Monocular Satellite Imagery and Deep Learning" @default.
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- W3206989807 doi "https://doi.org/10.1109/igarss47720.2021.9554938" @default.
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