167 research outputs found

    Integrated Applications of Geo-Information in Environmental Monitoring

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    This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society

    Advanced Computational Methods for Oncological Image Analysis

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    [Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

    Aeronautical engineering: A continuing bibliography with indexes (supplement 292)

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    This bibliography lists 675 reports, articles, and other documents recently introduced into the NASA scientific and technical information system database. Subject coverage includes the following: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Segmentation and Classification of Multimodal Imagery

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    Segmentation and classification are two important computer vision tasks that transform input data into a compact representation that allow fast and efficient analysis. Several challenges exist in generating accurate segmentation or classification results. In a video, for example, objects often change the appearance and are partially occluded, making it difficult to delineate the object from its surroundings. This thesis proposes video segmentation and aerial image classification algorithms to address some of the problems and provide accurate results. We developed a gradient driven three-dimensional segmentation technique that partitions a video into spatiotemporal objects. The algorithm utilizes the local gradient computed at each pixel location together with the global boundary map acquired through deep learning methods to generate initial pixel groups by traversing from low to high gradient regions. A local clustering method is then employed to refine these initial pixel groups. The refined sub-volumes in the homogeneous regions of video are selected as initial seeds and iteratively combined with adjacent groups based on intensity similarities. The volume growth is terminated at the color boundaries of the video. The over-segments obtained from the above steps are then merged hierarchically by a multivariate approach yielding a final segmentation map for each frame. In addition, we also implemented a streaming version of the above algorithm that requires a lower computational memory. The results illustrate that our proposed methodology compares favorably well, on a qualitative and quantitative level, in segmentation quality and computational efficiency with the latest state of the art techniques. We also developed a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are fused at the initial layers of deep neural networks as opposed to final layers. The early fusion architecture has fewer parameters and thereby reduces the computational time and GPU memory during training and inference. We also introduce a composite architecture that fuses features throughout the network. The methods were validated on four different datasets: ISPRS Potsdam, Vaihingen, IEEE Zeebruges, and Sentinel-1, Sentinel-2 dataset. For the Sentinel-1,-2 datasets, we obtain the ground truth labels for three classes from OpenStreetMap. Results on all the images show early fusion, specifically after layer three of the network, achieves results similar to or better than a decision level fusion mechanism. The performance of the proposed architecture is also on par with the state-of-the-art results

    Augmented Reality

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    Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

    Earth Resources: A continuing bibliography with indexes, issue 40

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    This bibliography lists 423 reports, articles, and other documents introduced into the NASA scientific and technical information system between October 1 and December 31, 1983. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

    The 1990 Johnson Space Center bibliography of scientific and technical papers

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    Abstracts are presented of scientific and technical papers written and/or presented by L. B. Johnson Space Center (JSC) authors, including civil servants, contractors, and grantees, during the calendar year of 1990. Citations include conference and symposium presentations, papers published in proceedings or other collective works, seminars, and workshop results, NASA formal report series (including contractually required final reports), and articles published in professional journals
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