Vegetation encroachment in power line corridors has multiple problems for modern energy-dependence societies, failures due to contact of lines and vegetation, can result in power outages and millions of dollars in losses. To address this problem, UAVs have emerged as a promising solution due to their ability to quickly and affordably monitor power line corridors for vegetation encroachment. However, the extensive manual task that requires analyzed each photo acquired by UAVs searching for the existence of vegetation encroachment has led many authors to propose using Deep Learning to automate the detection process. Despite the advantages of using UAVs and Deep Learning there is currently a lack of datasets that help to train any Deep Learning models. In this paper, we present a dataset for the segmentation of vegetation encroachment in power line corridors. We use RGB orthomosaics acquired in a rural road using a commercial UAV. RGB sliced images and multi-label mask for vegetation segmentation are provided. We provide detailed description of the image acquisition, the labeling task, the data augmentation techniques among other relevant details to produce the dataset. Researchers would benefit from using the proposed dataset to develop and improved strategies of vegetation encroachment monitoring using UAVs and Deep Learning.

Dataset contains four folders, where mask contains 3 classes (vegetation, power line corridors and background): -A total of 4 Orthomosaic, Mask and DSM full size - A folder with image and mask tessellated - A folder with image and mask tessellated with geometric augmentation - A folder with image and mask tessellated with spectral augmentation.