Unmanned Aerial Vehicles (UAVs) achieved a lot of momentum through their fast and tremendous evolution over the last decade. A multiplication of applications results from the use of the UAV imagery in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new compression, retargeting, and decision-making tools. To conduct such studies, in this era of big data and deep learning, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. To address this need, we introduce here the dataset EyeTrackUAV2 consisting of the collection of binocular gaze information through visualization of UAV videos for both free viewing and task-based attention conditions. An analysis of collected gaze positions provides recommendations for visual salience ground-truth generation. It also sheds light upon variations of saliency biases in UAV videos when opposed to conventional content, especially regarding the center bias.