On the truck dispatching problem

Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.

https://dl.acm.org/doi/pdf/10.1145/3017678

Data-Driven Techniques in Disaster Information Management

MIT Encyclopedia of the Cognitive Sciences

Systems and methods for controlling a movable object within an environment are provided. In one aspect, a method may comprise: determining, using at least one of a plurality of sensors carried by the movable object, an initial location of the movable object; generating a first signal to cause the movable object to navigate within the environment; receiving, using the at least one of the plurality of sensors, sensing data pertaining to the environment; generating, based on the sensing data, an environmental map representative of at least a portion of the environment; receiving an instruction to return to the initial location; and generating a second signal to cause the movable object to return to the initial location, based on the environmental map.

Multi-sensor environmental mapping

Systems and methods for controlling an unmanned aerial vehicle within an environment are provided. In one aspect, a system comprises one or more sensors carried by the unmanned aerial vehicle and configured to provide sensor data and one or more processors. The one or more processors can be individually or collectively configured to: determine, based on the sensor data, an environment type for the environment; select a flight mode from a plurality of different flight modes based on the environment type, wherein each of the plurality of different flight mode is associated with a different set of operating rules for the unmanned aerial vehicle; and cause the unmanned aerial vehicle to operate within the environment while conforming to the set of operating rules of the selected flight mode.

Context-based flight mode selection

Flow visualization is a well established branch of scientific visualization and it currently represents an invaluable resource to many fields, like automotive design, meteorology and medical imaging. Thanks to the capabilities of modern hardware, flow datasets are increasing in size and complexity, and traditional flow visualization techniques need to be updated and improved in order to deal with the upcoming challenges. A fairly recent trend to enhance the expressiveness of scientific visualization is to produce depictions of physical phenomena taking inspiration from traditional handcrafted illustrations: this approach is known as illustrative visualization, and it is getting a foothold in flow visualization as well. In this state of the art report we give an overview of the existing illustrative techniques for flow visualization, we highlight which problems have been solved and which issues still need further investigation, and, finally, we provide remarks and insights on the current trends in illustrative flow visualization.

Illustrative Flow Visualization: State of the Art, Trends and Challenges

The perception of transparency and the underlying neural mechanisms have been subject to extensive research in the cognitive sciences. However, we have yet to develop visualization techniques that optimally convey the inner structure of complex transparent shapes. In this paper, we apply the findings of perception research to develop a novel illustrative rendering method that enhances surface transparency nonlocally. Rendering of transparent geometry is computationally expensive since many optimizations, such as visibility culling, are not applicable and fragments have to be sorted by depth for correct blending. In order to overcome these difficulties efficiently, we propose the illustration buffer. This novel data structure combines the ideas of the A and G-buffers to store a list of all surface layers for each pixel. A set of local and nonlocal operators is then used to process these depth-lists to generate the final image. Our technique is interactive on current graphics hardware and is only limited by the available graphics memory. Based on this framework, we present an efficient algorithm for a nonlocal transparency enhancement that creates expressive renderings of transparent surfaces. A controlled quantitative double blind user study shows that the presented approach improves the understanding of complex transparent surfaces significantly.

/pdf/smart-transparency-for-illustrative-visualization-of-complex-kub34txzka.pdf

Smart Transparency for Illustrative Visualization of Complex Flow Surfaces

Uncertainties in flood predictions complicate the planning of mitigation measures. There is a consensus that many possible incident scenarios should be considered. For each scenario, a specific response plan should be prepared which is optimal with respect to criteria such as protection, costs, or realization time. None of the existing software tools is capable of creating large scenario pools, nor do they provide means for quick exploration and assessment of the associated plans. In this paper, we present an integrated solution that is based on multidimensional, time-dependent ensemble simulations of incident scenarios and protective measures. We provide scalable interfaces which facilitate and accelerate setting up multiple time-varying parameters for generating a pool of pre-cooked scenarios. In case of an emergency, disaster managers can quickly extract relevant information from the pool to deal with the situation at hand. An interactive 3D-view conveys details about how a response plan has to be executed. Linked information visualization and ranking views allow for a quick assessment of many plans. In collaboration with flood managers, we demonstrate the practical applicability of our solution. We tackle the challenges of planning mobile water barriers for protecting important infrastructure. We account for real-world limitations of available resources and handle the involved logistics problems.

/pdf/many-plans-multidimensional-ensembles-for-visual-decision-mrdywyy088.pdf

Many plans: multidimensional ensembles for visual decision support in flood management

Provided is point-based efficient three-dimensional (3D) information representation from a color image that is obtained from a general Charge-Coupled Device (CCD)/Complementary Metal Oxide Semiconductor (CMOS) camera, and a depth image that is obtained from a depth camera. A 3D image processing method includes storing a depth image associated with an object as first data of a 3D data format, and storing a color image associated with the object as color image data of a 2D image format, independent of the first data.

Method and apparatus for processing three-dimensional (3D) images

Artistic illustrations of important structures in fluid flow have a long-standing tradition and are appreciated as clearly perceivable, instructive, but still conveying all relevant information to the viewer. One important illustrative technique for such visualizations are cutaways. Currently cutaways are placed manually or using view-vector based approaches. We propose to optimize the visibility of important target features based on a degree-of-interest (DOI) function. The DOI is specified during interactive visual analysis, e.g., by brushing scatterplots. We show that the problem of placing cutaway boxes optimally is NP-hard in the number of boxes. To overcome this obstacle, we present an intelligent method to compute cutaways. Geometric cutaway objects are positioned using a view-dependent objective function which optimizes the visibility of all features. In order to approximate the optimal solution, we use a Monte Carlo method and exploit temporal coherence in dynamic scenes. Performance-critical parts are implemented on the GPU. The proposed method can be integrated easily into existing rendering frameworks and is general enough to be able to optimize other parameters besides cutaways as well. We evaluate the performance of the algorithm and provide a case study of vorticity visualization in a turbulent flow.

Robert Carnecky

Papers

Illustrative Flow Visualization: State of the Art, Trends and Challenges

Smart Transparency for Illustrative Visualization of Complex Flow Surfaces

Many plans: multidimensional ensembles for visual decision support in flood management

Method and apparatus for processing three-dimensional (3D) images

Intelligent cutaway illustrations