Estimates suggest that one hundred times more plastic enters the ocean annually than is found at the sea surface (Van Sebille et al., 2015). It is still unknown where the plastic goes and how much resides in the other ocean reservoirs, including the sea floor (Courtene-
Developing strategies to mitigate or to adapt to the threats of floods is an important topic in the context of climate changes. Many of the world’s cities are endangered due to rising ocean levels and changing precipitation patterns. It is therefore crucial to develop analytical tools that allow us to evaluate the threats of floods and to investigate the influence of mitigation and adaptation measures, such as stronger dikes, adaptive spatial planning, and flood disaster plans. Up until the present, analytical tools have only been accessible to domain experts, as the involved simulation processes are complex and rely on computational and data-intensive models. Outputs of these analytical tools are presented to practitioners (i.e., policy analysts and political decision-makers) on maps or in graphical user interfaces. In practice, this output is only used in limited measure because practitioners often have different information requirements or do not trust the direct outcome. Nonetheless, literature indicates that a closer collaboration between domain experts and practitioners can ensure that the information requirements of practitioners are better aligned with the opportunities and limitations of analytical tools. The objective of our work is to present a step forward in the effort to make analytical tools in flood management accessible for practitioners to support this collaboration between domain experts and practitioners. Our system allows the user to interactively control the simulation process (addition of water sources or influence of rainfall), while a realistic visualization allows the user to mentally map the results onto the real world. We have developed several novel algorithms to present and interact with flood data. We explain the technologies, discuss their necessity alongside test cases, and introduce a user study to analyze the reactions of practitioners to our system. We conclude that, despite the complexity of flood simulation models and the size of the involved data sets, our system is accessible for practitioners of flood management so that they can carry out flood simulations together with domain experts in interactive work sessions. Therefore, this work has the potential to significantly change the decision-making process and may become an important asset in choosing sustainable flood mitigations and adaptation strategies.
Image-to-geometry registration is the basis of many applications for texturing and interpreting 3D surface models. Feature-based matching is an established, automatic approach which creates 2D-3D correspondences based on salient points and their radiometric neighbourhood. This paper presents an experimental approach for assessing the accuracy of several matching algorithms in challenging imaging environments that are subject to significant outdoor illumination variations. Furthermore, a collection of accuracy assessment metrics and quality heuristics emerge from the presented approach to guide a user during the examination of registration results. As a result of the experiments, two novel salient point descriptor matching combinations outperform the standard scaleinvariant feature transform (SIFT) operator on the task of image-to-image and image-to-geometry registration under varying illumination conditions.The Photogrammetric Record
Rapid technological progress has made mobile devices increasingly valuable for scientific research. This paper outlines a versatile camera‐based water gauging method, implemented on smartphones, which is usable almost anywhere if 3D data is available at the targeted river section. After analysing smartphone images to detect the present water line, the image data is transferred into object space. Using the exterior orientation acquired by smartphone sensor fusion, a synthetic image originating from the 3D data is rendered that represents the local situation. Performing image‐to‐geometry registration using the true smartphone camera image and the rendered synthetic image, image parameters are refined by space resection. Moreover, the water line is transferred into object space by means of the underlying 3D information. The algorithm is implemented in the smartphone application “Open Water Levels”, which can be used on both high‐end and low‐cost devices. In a comprehensive investigation, the methodology is evaluated, demonstrating both its potential and remaining issues.
The excitations of a two-dimensional electron gas in quantum wells with intermediate carrier density (ne∼1011 cm-2), i.e., between the exciton-trion and the Fermi-sea range, are so far poorly understood. We report on an approach to bridge this gap by a magnetophotoluminescence study of modulation-doped (Cd,Mn)Te quantum well structures. Employing their enhanced spin splitting, we analyzed the characteristic magnetic-field behavior of the individual photoluminescence features. Based on these results and earlier findings by other authors, we present a new approach for understanding the optical transitions at intermediate densities in terms of four-particle excitations, the Suris tetrons, which were up to now only predicted theoretically. All characteristic photoluminescence features are attributed to emission from these quasiparticles when attaining different final states.
Abstract. The detection of finite-time coherent particle sets in Lagrangian trajectory data, using data-clustering techniques, is an active research field at the moment. Yet, the clustering methods mostly employed so far have been based on graph partitioning, which assigns each trajectory to a cluster, i.e. there is no concept of noisy, incoherent trajectories. This is problematic for applications in the ocean, where many small, coherent eddies are present in a large, mostly noisy fluid flow. Here, for the first time in this context, we use the density-based clustering algorithm of OPTICS (ordering points to identify the clustering structure; Ankerst et al., 1999) to detect finite-time coherent particle sets in Lagrangian trajectory data. Different from partition-based clustering methods, derived clustering results contain a concept of noise, such that not every trajectory needs to be part of a cluster. OPTICS also has a major advantage compared to the previously used density-based spatial clustering of applications with noise (DBSCAN) method, as it can detect clusters of varying density. The resulting clusters have an intrinsically hierarchical structure, which allows one to detect coherent trajectory sets at different spatial scales at once. We apply OPTICS directly to Lagrangian trajectory data in the Bickley jet model flow and successfully detect the expected vortices and the jet. The resulting clustering separates the vortices and the jet from background noise, with an imprint of the hierarchical clustering structure of coherent, small-scale vortices in a coherent, large-scale background flow. We then apply our method to a set of virtual trajectories released in the eastern South Atlantic Ocean in an eddying ocean model and successfully detect Agulhas rings. We illustrate the difference between our approach and partition-based k-means clustering using a 2D embedding of the trajectories derived from classical multidimensional scaling. We also show how OPTICS can be applied to the spectral embedding of a trajectory-based network to overcome the problems of k-means spectral clustering in detecting Agulhas rings.
ABSTRACT:Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration-and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm's accuracy compared to traditional methods, and the impact of computational-and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
334 Leonard St
Brooklyn, NY 11211
Copyright © 2023 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.