  Accurate detection and segmentation of transmission towers~(TTs) and power
lines~(PLs) from aerial images plays a key role in protecting power-grid
security and low-altitude UAV safety. Meanwhile, aerial images of TTs and PLs
pose a number of new challenges to the computer vision researchers who work on
object detection and segmentation -- PLs are long and thin, and may show
similar color as the background; TTs can be of various shapes and most likely
made up of line structures of various sparsity; The background scene, lighting,
and object sizes can vary significantly from one image to another. In this
paper we collect and release a new TT/PL Aerial-image (TTPLA) dataset,
consisting of 1,100 images with the resolution of 3,840$\times$2,160 pixels, as
well as manually labeled 8,987 instances of TTs and PLs. We develop novel
policies for collecting, annotating, and labeling the images in TTPLA.
Different from other relevant datasets, TTPLA supports evaluation of instance
segmentation, besides detection and semantic segmentation. To build a baseline
for detection and segmentation tasks on TTPLA, we report the performance of
several state-of-the-art deep learning models on our dataset. TTPLA dataset is
publicly available at https://github.com/r3ab/ttpla_dataset
