Aerial imagery plays an important role in land-use planning, population analysis, precision agriculture, and unmanned aerial vehicle tasks. However, existing aerial image datasets generally suffer from the problem of inaccurate labeling, single ground truth type, and few category numbers. In this work, we implement a simulator that can simultaneously acquire diverse visual ground truth data in the virtual environment. Based on that, we collect a comprehensive Virtual AeriaL Image Dataset named VALID, consisting of 6690 high-resolution images, all annotated with panoptic segmentation on 30 categories, object detection with oriented bounding box, and binocular depth maps, collected in 6 different virtual scenes and 5 various ambient conditions (sunny, dusk, night, snow and fog). To our knowledge, VALID is the first aerial image dataset that can provide panoptic level segmentation and complete dense depth maps. We analyze the characteristics of VALID and evaluate state-of-the-art methods for multiple tasks to provide reference baselines. The experiment results demonstrate that VALID is well presented and challenging. The dataset is available at https://sites.google.com/view/valid-dataset/.

The optimal state estimation problem for sampled-data systems with randomly sampled and delayed measurements is addressed in this paper. An optimal filter is presented first for the sampled-data system with randomly sampled and delay-free measurements from multiple sensors. The filter, which has been proved to be optimal in the sense of minimum estimation variance, updates the state estimation once new measurements are available. The result is applicable to a wide range of sampling cases of which the corresponding state estimation procedures are formulated separately. Discrete-time equivalent of the filter is also derived rigorously which makes it feasible for computer implementation. Furthermore, we extend the optimal filter and develop a sliding time window estimator through the measurement reorganization technique to deal with the situation of delayed measurements. Monte-Carlo simulations are carried out to demonstrate the effectiveness of the proposed approach.