Contrails (condensation trails) are line-shaped ice clouds caused by aircraft and are a substantial contributor to aviation-induced climate change. Contrail avoidance is potentially an inexpensive way to significantly reduce the climate impact of aviation. An automated contrail detection system is an essential tool to develop and evaluate contrail avoidance systems. In this article, we present a human-labeled dataset named OpenContrails to train and evaluate contrail detection models based on GOES-16 Advanced Baseline Imager (ABI) data. We propose and evaluate a contrail detection model that incorporates temporal context for improved detection accuracy. The human labeled dataset and the contrail detection outputs are publicly available on Google Cloud Storage atgs://goes_contrails_dataset.

This article presents two from-point visibility algorithms: one aggressive and one exact. The aggressive algorithm efficiently computes a nearly complete visible set, with the guarantee of finding all triangles of a front surface, no matter how small their image footprint. The exact algorithm starts from the aggressive visible set and finds the remaining visible triangles efficiently and robustly. The algorithms are based on the idea of generalizing the set of sampling locations defined by the pixels of an image. Starting from a conventional image with one sampling location at each pixel center, the aggressive algorithm adds sampling locations to make sure that a triangle is sampled at all the pixels it touches. Thereby, the aggressive algorithm finds all triangles that are completely visible at a pixel regardless of geometric level of detail, distance from viewpoint, or view direction. The exact algorithm builds an initial visibility subdivision from the aggressive visible set, which it then uses to find most of the hidden triangles. The triangles whose visibility status is yet to be determined are processed iteratively, with the help of additional sampling locations. Since the initial visible set is almost complete, and since each additional sampling location finds a new visible triangle, the algorithm converges in a few iterations.

Remote visualization has become both a necessity, as data set sizes have grown faster than computer network performance, and an opportunity, as laptop, tablet, and smartphone mobile computing platforms have become ubiquitous. However, the conventional remote visualization (CRV) approach of sending a new image from the server to the client for every view parameter change suffers from reduced interactivity. One problem is high latency, as the network has to be traversed twice, once to communicate the view parameters to the server and once to transmit the new image to the client. A second problem is reduced image quality due to aggressive compression or low resolution. We address these problems by constructing and transmitting enhanced images that are sufficient for quality output frame reconstruction at the client for a range of view parameter values. The client reconstructs thousands of frames locally, without any additional data from the server, which avoids latency and aggressive compression. We introduce animated depth images, which not only store a color and depth sample at every pixel, but also store the trajectory of the samples for a given time interval. Sample trajectories are stored compactly by partitioning the image into semi-rigid sample clusters and by storing one sequence of rigid body transformations per cluster. Animated depth images leverage sample trajectory coherence to achieve a good compression of animation data, with a small and user-controllable approximation error. We demonstrate animated depth images in the context of finite element analysis and SPH data sets.

Most images used in visualization are computed with the planar pinhole camera. This classic camera model has important advantages such as simplicity, which enables efficient software and hardware implementations, and similarity to the human eye, which yields images familiar to the user. However, the planar pinhole camera has only a single viewpoint, which limits images to parts of the scene to which there is direct line of sight. In this paper we introduce the curved ray camera to address the single viewpoint limitation. Rays are C1-continuous curves that bend to circumvent occluders. Our camera is designed to provide a fast 3-D point projection operation, which enables interactive visualization. The camera supports both 3-D surface and volume datasets. The camera is a powerful tool that enables seamless integration of multiple perspectives for overcoming occlusions in visualization while minimizing distortions.