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

共 15528 条记录，以下是501-510 订阅

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OPTIMIZING URBAN ROAD INSPECTION STRATEGY BASED ON GROUND PENETRATING RADAR data: A CASE STUDY

OPTIMIZING URBAN ROAD INSPECTION STRATEGY BASED ON GROUND PE...

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： He, Jinfeng Luo, Xianghuan Zhu, Song Zhu, Jiasong Li, Qingquan Shenzhen Univ Coll Civil & Transportat Engn Shenzhen Peoples R China MNR Key Lab Geoenvironm Monitoring Great Bay Area Shenzhen Peoples R China

ISBN: (纸本)9798350360332;9798350360325

This study, conducted in a Shenzhen district, assesses the necessity and cost-effectiveness of Ground Penetrating Radar (GPR) road inspections. The evaluation involves automatic identification of road defects, such as voids, cavities, and loose soil, followed by an analysis of their spatial distribution patterns, clustering effects, density, and correlation with the metro system. The study proposes an optimized strategy for large-scale road inspections based on the spatial distribution characteristics of identified defects, emphasizing the importance of more frequent inspections on vulnerable roads. The findings contribute to the efficient allocation of public funds, minimizing wasteful expenditures, and enhancing road operational safety.

关键词： GPR road inspection road defects inspection strategy cost effectiveness

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HIGH SPATIAL RESOLUTION FOR CROP YIELD PREDICTION IN large FARMING SYSTEMS: A NECESSITY OR ADDITIONAL OVERHEAD

HIGH SPATIAL RESOLUTION FOR CROP YIELD PREDICTION IN LARGE F...

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Ofori-Ampofo, Stella Kuzu, Ridvan Salih Zhu, Xiao Xiang Tech Univ Munich Munich Germany German Aerosp Ctr DLR Cologne Germany

ISBN: (纸本)9798350320107

The availability of open-access satellite data and advancements in machine learning techniques has exhibited significant potential in crop yield prediction. In the context of large farming systems and county-level predictions, it is customary to rely on coarse-resolution satellite images. However, these images often lack the sufficient textural detail to accurately summarise spatial information. This research aims to evaluate the advantages of enhanced spatial resolution by conducting a comparative analysis between coarse-resolution, high-temporal-frequency MODIS data and relatively high-resolution, low-temporal-frequency Landsat data for predicting corn yield in the USA. We benchmark this comparison against several models in a spatial versus non-spatial input data context. Our results suggest that, the use of high-spatial resolution for county-level yield prediction in large farming systems is not beneficial and the models explored are unable to generalize well to drought-struck years.

关键词： remote sensing crop yield prediction spatio-temporal resolution food security machine learning convolutional neural network recurrent neural network

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Exploring Sampler Strategies in Unsupervised Person Re-identification Training: Insights and Performance analysis 33

Exploring Sampler Strategies in Unsupervised Person Re-ident...

引用

33rd International symposium on Industrial Electronics (ISIE)

作者： Cao, Ge Tang, Qing Dat, Tran Tien Russo, Ashraf Uddin Priadana, Adri Jo, Kanghyun Univ Ulsan Dept Elect Elect & Comp Engn Ulsan South Korea INTERX Data Sci Grp Ulsan South Korea

ISBN: (纸本)9798350394085;9798350394092

Person re-identification holds significant research value within supervised systems characterized by non-overlapping multiple cameras. In recent years, unsupervised learning has made notable strides and has gradually approached the training efficacy of supervised learning. This paper focuses on exploring the influence and analysis of various sampling strategies on overall unsupervised training. We initially delineate a proxy-level memory bank scheme based on camera labels and employ a hard sample mining strategy for selecting negative pairs in a contrastive learning loss. Various sampling strategies, Random sampling, triplet sampling with dissimilar labels, and group sampling yield markedly distinct outcomes across three large-scale datasets, i.e. Market-1501, DukeMTMC-reID, and MSMT17. Detailed analysis and discussion of these results are provided in this study.

关键词： Person Re-identification unsupervised learning sampling strategy

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Graphical Representation Through a User Interface for In Situ Scientific visualization with Ascent

Graphical Representation Through a User Interface for In Sit...

引用

ieee symposium on large data analysis and visualization (LDAV)

作者： Colleen Heinemann Jefferson Amstutz Joseph A. Insley Victor A. Mateevitsi Michael E. Papka Silvio Rizzi University of Illinois at Urbana-Champaign NVIDIA Argonne National Laboratory University of Illinois Chicago Northern Illinois University

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

This work presents a graphical user interface (GUI) designed to allow users to interactively create scientific visualizations and workflows with the in situ visualization software, Ascent [5]. Traditionally, in situ pipelines are configured through a YAML file, which can be cumbersome. Our GUI offers an alternative by automating the conversion of user inputs into a format that Ascent can process, eliminating the need for manual file editing. This interactive capability increases the potential for a more diverse group of users to leverage Ascent's powerful visualization tools.

关键词： data analysis Pipelines data visualization Organizations Software Graphical user interfaces

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ANALYTICAL CLASSIFICATION PERFORMANCE analysis OF MACHINE-LEARNING-BASED SHIP DETECTION FROM OPTICAL SATELLITE IMAGERY

ANALYTICAL CLASSIFICATION PERFORMANCE ANALYSIS OF MACHINE-LE...

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Barretta, Domenico Millefiori, Leonardo M. Braca, Paolo Univ Campania L Vanvitelli Dept Engn Aversa Italy NATO STO CMRE Res Dept La Spezia Italy

ISBN: (纸本)9798350360332;9798350360325

We investigate the performance of machine learning (ML) binary classification models in terms of error probabilities to detect targets (specifically, ships) from optical satellite imagery. The ML approach uses a data-Driven Decision Function (D3F), learned during training, as a decision statistic. Inspired by the large Deviations analysis (LDA), we observe that, under suitable conditions, the detection error probabilities decrease as the number of pixels occupied by the target(s) in the image increases. Coherent with the LDA, the D3F follows a Gaussian distribution, conditioned to parameters like the background. We propose a methodology to set a desired false alarm rate and estimate the correct decision probability, beneficial for various remote sensing applications, including maritime surveillance.

关键词： Statistical Hypothesis Testing Machine Learning Earth Observation Ship Detection Analytical Performance

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COUNTRY-SCALE MAPPING OF FOREST PARAMETERS USING DEEP LEARNING AND TANDEM-X INSAR data

COUNTRY-SCALE MAPPING OF FOREST PARAMETERS USING DEEP LEARNI...

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Carcereri, Daniel Rizzolii, Paola Dell'Amore, Luca Bueso-Bello, Jose-Luis Ienco, Dino Bruzzone, Lorenzo German Aerosp Ctr DLR Microwaves & Radar Inst Cologne Germany Univ Trento UNITN Trento Italy Univ Montpellier Natl Res Inst Agr Food & Environm INRAE Montpellier France

ISBN: (纸本)9798350360332;9798350360325

Highly accurate estimates of canopy height (CH) and above ground biomass (AGB) are key parameters for forest disturbance analysis, resource monitoring, and carbon flux analyses. In this work we present a deep learning-based approach for mapping CH and AGB on country-scales from single-baseline, single-polarization, single-pass TanDEM-X InSAR data. The proposed approach consists in a convolutional neural network (CNN), trained and validated on the five test-sites covered by the 2016 AfriSAR campaign. The resulting performance is in line or better than those of current state-of-the-art approaches. The framework is subsequently deployed on a large-scale to map the entire country of Gabon (in West Central Africa), showcasing the flexibility, scalability, and accuracy of our proposed approach for forest parameter estimation.

关键词： Forest monitoring forest height above ground biomass deep learning InSAR TanDEM-X

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MAPPING ABOVEGROUND BIOMASS STOCKS ACROSS AFRICA'S MIOMBO WOODLANDS USING MULTI-SCALE LIDAR AND SATELLITE data

MAPPING ABOVEGROUND BIOMASS STOCKS ACROSS AFRICA'S MIOMBO WO...

引用

ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Rodriguez-Veiga, Pedro Bernotaitel, Gabija Burt, Andrew Demol, Miro Foing, Victoria Hansen, Johannes Marshall, Abie Napiorkowskal, Milena Upham, Robin Sylvera Ltd London England Univ Leicester Leicester Leics England

ISBN: (纸本)9798350360332;9798350360325

Aboveground biomass (AGB), traditionally estimated across large scales using forest inventory approaches, is an essential climate variable used to monitor the carbon released and sequestered by woody vegetation. However, forest inventory methods rely on tree-level allometries which are often biassed. We estimate AGB density (AGBD) wall-to-wall over African Miombo woodland using accurate and unbiased AGBD measurements derived from multi-scale lidar technology (MSL), which circumvent the use of allometric models, in combination with spaceborne lidar data and satellite imagery. A comparative analysis of the AGB stocks for the Zambezia region (Mozambique) is performed against allometric-derived AGBD reference datasets and biome-average emission factor values found in the literature. Allometric-based approaches show underestimations in AGB stocks ranging from -3% to -33% of the AGB stocks estimated using the reference MSL AGBD dataset.

关键词： forest biomass machine-learning lidar

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Principal Component analysis visualization and State Discovery with Soil data 12

Principal Component Analysis Visualization and State Discove...

引用

12th ieee International Conference on Intelligent data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Sirola, Miki Koskinen, Markku Polvinen, Tatu Pihlatie, Mari University of Helsinki HelsinkiFI-00014 Finland

ISBN: (纸本)9798350358056

data exploration helps to gain understanding of the dataset and the system itself. There are methodologies to handle large number of sensors as well. In this paper operational states are defined to interpret physical behaviour in a soil ecosystem. Dimensionality reduction is achieved with Principal Component analysis (PCA) method giving another view to the soil dataset from spring term. K-means algorithm groups data densities by clustering the data. This grouping is the basis for defining operational states in the system. Soil data as a part of an ecosystem involves specific features. In the applied approach dynamic visualization including animations constitute an important exploration view. All experiments are realized in Jupyter programming environment with Python 3 programming language. Related literature about data visualization is reviewed. Combining methods and tools with this data as a result soil ecosystem features are recognized. © 2023 ieee.

关键词： Principal Component analysis Soil data State Discovery visualization

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Visual Comparison of Text Sequences Generated by large Language Models

Visual Comparison of Text Sequences Generated by Large Langu...

引用

ieee Conference on visualization in data Science (VDS)

作者： Sevastjanova, Rita Vogelbacher, Simon Spitz, Andreas Keim, Daniel El-Assady, Mennatallah Univ Konstanz Constance Germany ETH AI Ctr Zurich Switzerland

ISBN: (纸本)9798350330205

Causal language models have emerged as the leading technology for automating text generation tasks. Although these models tend to produce outputs that resemble human writing, they still suffer from quality issues (e.g., social biases). Researchers typically use automatic analysis methods to evaluate the model limitations, such as statistics on stereotypical words. Since different types of issues are embedded in the model parameters, the development of automated methods that capture all relevant aspects remains a challenge. To tackle this challenge, we propose a visual analytics approach that supports the exploratory analysis of text sequences generated by causal language models. Our approach enables users to specify starting prompts and effectively groups the resulting text sequences. To this end, we leverage a unified, ontology-driven embedding space, serving as a shared foundation for the thematic concepts present in the generated text sequences. Visual summaries provide insights into various levels of granularity within the generated data. Among others, we propose a novel comparison visualization that slices the embedding space and represents the differences between two prompt outputs in a radial layout. We demonstrate the effectiveness of our approach through case studies, showcasing its potential to reveal model biases and other quality issues.

关键词： Causal Language Models Text Generation Prompt Output Comparison

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Standardized data-Parallel Rendering Using ANARI

Standardized Data-Parallel Rendering Using ANARI

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ieee symposium on large data analysis and visualization (LDAV)

作者： Ingo Wald Stefan Zellmann Jefferson Amstutz Qi Wu Kevin Griffin Milan Jaros Stefan Wesner NVIDIA University of Cologne Davis University of California IT4Innovations VSB Technical University of Ostrava

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

We propose and discuss a paradigm that allows for expressing data-parallel rendering with the classically non-parallel ANARI API. We propose this as a new standard for data-parallel rendering, describe two different implementations of this paradigm, and use multiple sample integrations into existing applications to show how easy it is to adopt, and what can be gained from doing so.

关键词： data analysis Prototypes data visualization Rendering (computer graphics) Standards

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