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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

共 15535 条记录，以下是741-750 订阅

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HISTORICAL PRODUCT DRIVEN large-SCALE HIGH-RESOLUTION LAND COVER AND WETLAND CLASSIFICATION

HISTORICAL PRODUCT DRIVEN LARGE-SCALE HIGH-RESOLUTION LAND C...

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Zeng, Siqi Huang, Xueying Wu, Yingxin Xu, Xian Liu, Yinhe Ma, Ailong Zhong, Yanfei Wuhan Univ Wuhan 430079 Hubei Peoples R China

ISBN: (纸本)9798350360332;9798350360325

Understanding land cover dynamics, especially in wetlands, is crucial for environmental monitoring and ecosystem management, given the threats posed by climate change and human activities. Despite the existence of global-scale land cover maps and detailed wetland thematic maps, a gap remains in their integration, limiting comprehensive ecosystem analysis. Typically, these methods utilize medium-resolution data, which fail to capture the finer details of land cover and wetland interfaces, highlighting the need for high-resolution mapping. As high-resolution mapping becomes more prevalent, the classification of land cover and wetland classes grows increasingly complex. Deep learning methods, particularly semantic segmentation, have emerged as solutions, yet they require extensive training labels, which are challenging and resource-intensive to acquire. This paper proposes a novel framework utilizing historical land cover products as training labels for high-resolution mapping, addressing the challenges of resolution mismatches and semantic errors. The developed cross-resolution mapping dataset, evaluated against a manually labeled test set, demonstrates the framework's effectiveness in providing detailed classification at a provincial scale.

关键词： Land Cover Wetland High-Resolution Remote Sensing Historical Product Label Noise

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SCALING AUTOMATIC VECTOR data ALIGNMENT TO SATELLITE IMAGERY

SCALING AUTOMATIC VECTOR DATA ALIGNMENT TO SATELLITE IMAGERY

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Potnis, Abhishek Lunga, Dalton Dias, Philipe Yang, Lexie Arndt, Jacob Bowman, Jordan Oak Ridge Natl Lab Geospatial Sci & Human Secur Div Oak Ridge TN 37830 USA

ISBN: (纸本)9798350320107

Given the tremendous volume of accessible Earth Observation (EO) data, there is a need to develop scalable Geospatial Artificial Intelligence (GeoAI) solutions for time-sensitive applications. Scalability in this context refers to rapidly processing large-scale EO data using high performance computing resources. Accurate mapping of the built environment from remote sensing (RS) imagery has been one of the crucial components in GeoAI workflows for a wide spectrum of humanitarian applications. Derived vector data of built environment is often leveraged for disaster preparedness and response activities. However, factors such as differences in ortho-rectification, atmospheric conditions and human error, results in spatial misalignment between vector data and the timely available RS imagery. Model training for downstream tasks such as object detection, change analysis, etc., is negatively impacted due to such spatial misalignment. Although there has been progress towards automatic alignment of vector data, the lack of scalability remains an open research challenge. This paper proposes to leverage parallel computing to optimize an automatic vector data alignment workflow. It further employs CPU-level multi-core parallelism for improving the performance of the workflow for scalable built environment mapping. We report observations and discuss findings from the preliminary experiments performed on the Summit Supercomputer.

关键词： scalable geospatial vector data alignment raster remote sensing imagery

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Raproto: An Open-Source Platform for Rapid Prototyping with Wearable Devices 20

Raproto: An Open-Source Platform for Rapid Prototyping with ...

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20th International Conference on Body Sensor Networks

作者： Hamid, Tarek Helm, Kimberly Choi, Hyonyoung Park, Jean Kendell, Claire Cummings, Stephanie Messe, Steve Modri, Stefanie Lee, Insup Weimer, James Watson, Amanda Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA Univ Penn Dept Comp & Informat Sci Philadelphia PA USA Univ Penn Sch Med Philadelphia PA 19104 USA Univ Penn Sch Nursing Philadelphia PA USA

ISBN: (纸本)9798331530143

Advances in wearable technology have enabled ubiquitous use of wearable devices in remote patient monitoring, particularly in clinical trials. Because of the reliance on high-quality data in these endeavors, the first and often the most time-consuming step is to build a data collection system. While many systems have been developed to address this, they are often highly specific and customized to the task at hand, and are often not generalized enough to support other tasks. To remedy this, we developed Raproto, an open-source easy-to-use rapid prototyping platform that does not require the time, effort, and expertise needed for custom development. The Raproto platform consists of three components, the wearable device(s), communication protocol, and remote storage. These components support the collection, transmission, storage, analysis, and visualization of large-scale data with applications from smaller-scale research studies to large clinical trials. To reduce the burden of device and application development, we created multipurpose and customizable smartwatch applications on both the Android and Tizen operating systems. We evaluate our platform in a lab setting as well as in two real-world case studies. Overall, we find that we can collect data using our application for over 24 hours on a single charge and there is little to no data loss, thus making it an ideal tool to preface customized device development for real-world impact and commercialization.

关键词： wearables data collection Android Tizen remote patient monitoring clinical trials

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AD-HOC SITUATIONAL AWARENESS DURING FLOODS USING REMOTE SENSING data AND MACHINE LEARNING METHODS

AD-HOC SITUATIONAL AWARENESS DURING FLOODS USING REMOTE SENS...

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Wieland, Marc Merkle, Nina Schneibel, Anne Henry, Corentin Lechner, Konstanze Yuan, Xiangtian Azimi, Seyed Majid Gstaiger, Veronika Martinis, Sandro German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Oberpfaffenhofen Germany German Aerosp Ctr DLR Remote Sensing Technol Inst IMF Oberpfaffenhofen Germany

ISBN: (纸本)9798350320107

Recent advances in machine learning and the rise of new large-scale remote sensing datasets have opened new possibilities for automation of remote sensing data analysis that make it possible to cope with the growing data volume and complexity and the inherent spatio-temporal dynamics of disaster situations. In this work, we provide insights into machine learning methods developed by the German Aerospace Center (DLR) for rapid mapping activities and used to support disaster response efforts during the 2021 flood in Western Germany. These include specifically methods related to systematic flood monitoring from Sentinel-1 as well as road-network extraction, object detection and damage assessment from very high-resolution optical satellite and aerial images. We discuss aspects of data acquisition and present results that were used by first responders during the flood disaster.

关键词： Disaster response flood monitoring road network extraction object detection damage assessment

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Sub-Linear Time Sampling Approach for large-Scale data visualization Using Reinforcement Learning

Sub-Linear Time Sampling Approach for Large-Scale Data Visua...

引用

ieee symposium on large data analysis and visualization (LDAV)

作者： Ayan Biswas Arindam Bhattacharya Yi-Tang Chen Han-Wei Shen Los Alamos National Laboratory Uber Technologies Los Alamos National Laboratory Ohio State University Ohio State University

As the compute capabilities of the modern supercomputers continue to rise, domain scientists are able to run their simulations at very fine spatial and temporal resolutions. Compared to the compute speeds, the I/O bandwidth continues to lag by orders of magnitude. This necessitates that the analysis and data reduction are performed in situ while the simulation generated data is still at the supercomputer memory. Recently, intelligent data-driven sampling schemes have been proposed by visualization researchers. These sampling methods are scalable, in situ capable and able to identify the feature regions of the data. Although powerful, these sampling schemes need to traverse through the data sets at least twice, which can become problematic as the simulations start to touch the exascale capabilities. In this paper, we propose to use a reinforcement learning-based approach to devise a sub-linear time sampling algorithm. Using multi-arm bandits, we show that we can identify samples that are of similar or better quality compared to the existing methods while only touching a small fraction of the original data. We use multiple simulation data sets to show the efficacy of our proposed method.

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HYPERSPECTRAL data SCARCITY PROBLEM FROM A SUPER RESOLUTION PERSPECTIVE: data AUGMENTATION analysis AND SCHEME

HYPERSPECTRAL DATA SCARCITY PROBLEM FROM A SUPER RESOLUTION ...

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Aburaed, Nour Alkhatib, Mohammed Q. Marshall, Stephen Zabalza, Jaime Al Ahmad, Hussain Univ Strathclyde Dept Elect & Elect Engn Glasgow Scotland Univ Dubai Coll Engn & IT Dubai U Arab Emirates

ISBN: (纸本)9798350320107

Hyperspectral Single Image Super Resolution is an important field of research due to the low spatial resolution of Hyperspectral Images (HSI) that limits their usability. Deep Convolutional Neural Networks (DCNNs) have been commonly used for SISR tasks, however, a large dataset is typically needed for training. Because of HSI data scarcity, training DCNNs for HSI-SISR becomes a challenging task. In this study, HSI data scarcity problem is tackled from an SISR perspective via data Augmentation (DA). Several DA techniques are reviewed in this context, and a new DA technique called CutMixBlur is introduced. The best techniques are decided based on maximizing Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measurement (SSIM) while minimizing Spectral Angle Mapper (SAM). The aim is to enhance HSI spatially without distorting their unique spectral signature. Experiments on Pavia University and Indian Pines datasets show that CutMixBlur boosts all quality metrics. Additionally, applying DA techniques by randomly stacking their effect significantly enhances the performance of SISR DCNNs, particularly the 3D-SRCNN.

关键词： Single Image Super Resolution Augmentation Hyperspectral 3D-CNN SRCNN

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Improving Understanding of Biocide Availability in Facades through Immersive Analytics 21

Improving Understanding of Biocide Availability in Facades t...

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21st ieee International symposium on Mixed and Augmented Reality (ISMAR)

作者： Nouri, Negar Kalamkar, Snehanjali Farzinnejad, Forouzan Biener, Verena Schick, Fabian Kalkhof, Stefan Grubert, Jens Coburg Univ Appl Sci & Arts Coburg Germany

ISBN: (数字)9781665453653

ISBN: (纸本)9781665453653

The durability of facades is heavily affected by multiple factors like microbial growth and weather conditions among others. Biocides are often used to resist these factors and protect the facades. However, the biocides get washed out due to rains and other factors like geometric structure of the facade, orientation of the building. It is therefore, important to understand how these factors affect the durability of facades, leading to a requirement of expert analysis. In this paper, we propose a technical pipeline and a set of interaction techniques to support data analysis within the immersive environment for our case study. Our technical pipeline mainly consists of three steps: 3D reconstruction, embedding sensor data and visualization and interaction techniques. We made a formative evaluation of our prototype to get insights from microbiology, biology and VR experts. The remarks from the experts and the results of the evaluation suggest that an immersive analytic system in our case study could be beneficial for both experts and non-expert users.

关键词： Immersive Analytics Biocide Availability visualization Techniques Virtual Reality 3D Reconstruction Sensor data

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SoK: History is a Vast Early Warning System: Auditing the Provenance of System Intrusions 44

SoK: History is a Vast Early Warning System: Auditing the Pr...

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44th ieee symposium on Security and Privacy (SP)

作者： Inam, Muhammad Adil Chen, Yinfang Goyal, Akul Liu, Jason Mink, Jaron Michael, Noor Gaur, Sneha Bates, Adam Hassan, Wajih Ul Univ Illinois Champaign IL 61820 USA Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9781665493369

Auditing, a central pillar of operating system security, has only recently come into its own as an active area of public research. This resurgent interest is due in large part to the notion of data provenance, a technique that iteratively parses audit log entries into a dependency graph that explains the history of system execution. Provenance facilitates precise threat detection and investigation through causal analysis of sophisticated intrusion behaviors. However, the absence of a foundational audit literature, combined with the rapid publication of recent findings, makes it difficult to gain a holistic picture of advancements and open challenges in the area. In this work, we survey and categorize the provenance-based system auditing literature, distilling contributions into a layered taxonomy based on the audit log capture and analysis pipeline. Recognizing that the Reduction Layer remains a key obstacle to the further proliferation of causal analysis technologies, we delve further on this issue by conducting an ambitious independent evaluation of 8 exemplar reduction techniques against the recently-released DARPA Transparent Computing datasets. Our experiments uncover that past approaches frequently prune an overlapping set of activities from audit logs, reducing the synergistic benefits from applying them in tandem;further, we observe an inverse relation between storage efficiency and anomaly detection performance. However, we also observe that log reduction techniques are able to synergize effectively with data compression, potentially reducing log retention costs by multiple orders of magnitude. We conclude by discussing promising future directions for the field.

关键词： data-Provenance Intrusion-Detection System-Auditing

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Processing and Analyzing Drilling Tools Information by NI DIAdem Software 5

Processing and Analyzing Drilling Tools Information by NI DI...

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5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

作者： Li, Fei Zhang, Ruiqiong Li, Meng School of Electronic Engineering Xi'An Shiyou University Xi'an710065 China

ISBN: (纸本)9798350336030

To promote the digital and intelligent development of the oil and gas industry, an automatic data analysis scheme is proposed to solve the problems of large amounts of drilling data, mainly relying on manual analysis and process duplication. This article focuses on a tool information processing program created by NI DIAdem, which automatically analyzes and identifies the tool's status based on input data and can generate reports. The designed program can import data from different file formats, convert it to a unified standard format, and store it in DIAdem. According to the characteristics of the data, the program will use the established classification model based on logistic regression to screen and classify the data and then process the data according to the predefined mathematical way. Finally, the processing results will be saved in the preset report to achieve the effect of data visualization and decision-making aid and improve work efficiency. © 2023 ieee.

关键词： data visualization

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Design and Implementation of Interactive PyQt5-Based Air Pollutant Spectral analysis Software 3

Design and Implementation of Interactive PyQt5-Based Air Pol...

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3rd ieee International Conference on Computer Communication and Artificial Intelligence, CCAI 2023

作者： Wang, Chuming Nankai University College of Software Tianjin China

ISBN: (纸本)9798350335262

Air pollution is an important issue in the environmental field. To control air pollution, it is particularly critical to clarify the composition of air pollutants in corresponding areas. Using atmospheric spectral data and machine learning methods, the composition of pollutants in the atmosphere can be quickly predicted. This process requires the processing of a large amount of spectral data and file management. In response to these requirements, multiple functional interfaces were designed in this work, and interface development and logic writing were carried out by combining the QT designer with code. data management was carried out in combination with the MySQL database, and spectral diagrams were drawn in combination with the Matplotlib library. Interactive air pollution analysis software based on PyQt5 was created. The software can realize data storage, retrieval, modification, and deletion and facilitate the processing and visualization of a large amount of spectral data. Combined with the model of spectral component prediction and concentration identification, the corresponding interactive operation can be realized. © 2023 ieee.

关键词： Air pollution

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