3-D Visualization of Municipal Regeneration using ArcScene

2017 ◽  
Author(s):  
Melissa Fedrigo
Keyword(s):  
Physical Environment ◽  
Large Scale ◽  
3D Visualization ◽  
Business Owners ◽  
Visualization Tool ◽  
Area Of Interest ◽  
Scale Regeneration

This presentation illustrates the use of ArcScene as a 3-D visualization tool in the context of prospective changes to the physical environment of a small municipal community. Before large-scale regeneration is agreed on or fully implemented in a municipality, there is an emphasis on the appearance of the area of interest over the course of the implementation. This presentation will discuss the benefits and limitations of 3D visualization for pre-construction, during, and post-construction presentation to municipal residents, members of council and impacted business owners.

scholarly journals A novel 3D visualization tool for large-scale neural networks

2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Alexander Jones ◽  
Justin Cardoza ◽  
Denver J Liu ◽  
Laurence C Jayet Bray ◽  
Sergiu M Dascalu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
3D Visualization ◽  
Visualization Tool

Bullying, School Violence, and Climate in Evolving Contexts

2018 ◽  
Author(s):  
Ron Avi Astor ◽  
Rami Benbenisthty
Keyword(s):  
School Violence ◽  
School Safety ◽  
Media Coverage ◽  
Large Scale ◽  
Ecological Model ◽  
Empirical Studies ◽  
Practical Applications ◽  
Teacher Student ◽  
Area Of Interest ◽  
New Research

Since 2005, the bullying, school violence, and school safety literatures have expanded dramatically in content, disciplines, and empirical studies. However, with this massive expansion of research, there is also a surprising lack of theoretical and empirical direction to guide efforts on how to advance our basic science and practical applications of this growing scientific area of interest. Parallel to this surge in interest, cultural norms, media coverage, and policies to address school safety and bullying have evolved at a remarkably quick pace over the past 13 years. For example, behaviors and populations that just a decade ago were not included in the school violence, bullying, and school safety discourse are now accepted areas of inquiry. These include, for instance, cyberbullying, sexting, social media shaming, teacher–student and student–teacher bullying, sexual harassment and assault, homicide, and suicide. Populations in schools not previously explored, such as lesbian, gay, bisexual, transgender, and queer students and educators and military- and veteran-connected students, become the foci of new research, policies, and programs. As a result, all US states and most industrialized countries now have a complex quilt of new school safety and bullying legislation and policies. Large-scale research and intervention funding programs are often linked to these policies. This book suggests an empirically driven unifying model that brings together these previously distinct literatures. This book presents an ecological model of school violence, bullying, and safety in evolving contexts that integrates all we have learned in the 13 years, and suggests ways to move forward.

scholarly journals Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2564
Author(s):  
Mauro Martini ◽  
Vittorio Mazzia ◽  
Aleem Khaliq ◽  
Marcello Chiaberge
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Domain Adaptation ◽  
Learning Approaches ◽  
Atmospheric Conditions ◽  
Practical Applications ◽  
Area Of Interest ◽  
Multi Temporal ◽  
Significant Performance ◽  
Adversarial Training

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

KG3D: An Interactive 3D Visualization Tool for Knowledge Graphs

2019 ◽  
pp. 886-889
Author(s):  
Dawei Xu ◽  
Lin Wang ◽  
Xin Wang ◽  
Dianquan Li ◽  
Jianpeng Duan ◽  
...  
Keyword(s):  
3D Visualization ◽  
Visualization Tool ◽  
Knowledge Graphs ◽  
Interactive 3D

scholarly journals Olomouc - Possibilities of Geovisualization of the Historical City

2011 ◽  
Vol 6 ◽  
pp. 267-274
Author(s):  
Stanislav Popelka ◽  
Alžběta Brychtová
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
18Th Century ◽  
3D Visualization ◽  
3D Models ◽  
Google Earth ◽  
Point Of View ◽  
World Heritage Site ◽  
Historical Maps ◽  
The City

Olomouc, nowadays a city with 100,000 inhabitants, has always been considered as one of the most prominent Czech cities. It is a social and economical centre, which history started just about the 11th century. The present appearance of the city has its roots in the 18th century, when the city was almost razed to the ground after the Thirty years’ war and a great fire in 1709. After that, the city was rebuilt to a baroque military fortress against Prussia army. At the beginning of the 20th century the majority of the fortress was demolished. Character of the town is dominated by the large number of churches, burgher’s houses and other architecturally significant buildings, like a Holy Trinity Column, a UNESCO World Heritage Site. Aim of this project was to state the most suitable methods of visualization of spatial-temporal change in historical build-up area from the tourist’s point of view, and to design and evaluate possibilities of spatial data acquisition. There are many methods of 2D and 3D visualization which are suitable for depiction of historical and contemporary situation. In the article four approaches are discussed comparison of historical and recent pictures or photos, overlaying historical maps over the orthophoto, enhanced visualization of historical map in large scale using the third dimension and photorealistic 3D models of the same area in different ages. All mentioned methods were geolocalizated using the Google Earth environment and multimedia features were added to enhance the impression of perception. Possibilities of visualization, which were outlined above, were realized on a case study of the Olomouc city. As a source of historical data were used rapport plans of the bastion fortress from the 17th century. The accuracy of historical maps was confirmed by cartometric methods with use of the MapAnalyst software. Registration of the spatial-temporal changes information has a great potential in urban planning or realization of reconstruction and particularly in the propagation of the region and increasing the knowledge of citizens about the history of Olomouc.

scholarly journals Tinjauan Skala Ukuran Perusahaan terhadap Perilaku Entrepreneurial Marketing serta kaitanya dengan Dominasi Dimensi Opportunity dan Growth Orientation

2019 ◽  
Vol 4 (2) ◽  
pp. 93-104 ◽  
Author(s):  
Christina Whidya Utami ◽  
Hendro Susanto
Keyword(s):  
Large Scale ◽  
National Sample ◽  
Business Owners ◽  
Small Scale ◽  
Full Time ◽  
Small Companies ◽  
Snowball Sampling ◽  
Dominant Dimension ◽  
Entrepreneurial Marketing ◽  
Growth Orientation

This study aims to bridge the gap in the literature by examining the dimensions of Growth Orientation, Opportunity Orientation, Total customer focus dimensions, Value creation Networking, Informal Market Analysis, Closness to The Market on large and small company scale related to Entrepreneurial Marketing (EM) behavior. Thus the objectives to be developed in this study are: analyze how EM behavior in small companies compared to large companies? The dimensions of whether in entrepreneurial marketing are dominant in the context of small and large scale companies. The questionnaire was aimed at a national sample of 406 business owners in Indonesia, spread in 8 provinces. Small companies are defined as companies that employ at least 9 fewer full-time employees other than their owners, while large companies are companies that employ more than 9 full-time employees other than their owners. Snowball sampling is used to determine the selected respondent. The results showed that there are differences in entrepreneurial marketing behavior between large companies and small companies, where small scale companies have lower entrepreneurial marketing behavior compared to large scale companies. For small scale companies, it shows that opportunity orientation is the most dominant dimension, whereas in large scale companies it shows that growth orientation is the most dominant dimension.

scholarly journals CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

2017 ◽  
Author(s):  
Venkata Manem ◽  
George Adam ◽  
Tina Gruosso ◽  
Mathieu Gigoux ◽  
Nicholas Bertos ◽  
...  
Keyword(s):  
Stromal Cells ◽  
Large Scale ◽  
Network Visualization ◽  
Visualization Tool ◽  
Biological Processes ◽  
Web Based ◽  
Link Type ◽  
A Cell ◽  
Large Scale Networks ◽  
User Friendly

ABSTRACTBackground:Over the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.Results:CrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.Conclusion:CrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.Availability:The web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.Contact:[email protected]

scholarly journals Physical Transport Processes that Affect the Distribution of Oil in the Gulf of Mexico: Observations and Modeling

Oceanography ◽  
2021 ◽  
Vol 34 (1) ◽  
pp. 58-75
Author(s):  
Michel Boufadel ◽  
◽  
Annalisa Bracco ◽  
Eric Chassignet ◽  
Shuyi Chen ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Physical Environment ◽  
Large Scale ◽  
Transport Processes ◽  
Small Scale ◽  
Surface Mixed Layer ◽  
Physical Processes ◽  
Mixing Processes ◽  
Near Surface ◽  
Wide Range

Physical transport processes such as the circulation and mixing of waters largely determine the spatial distribution of materials in the ocean. They also establish the physical environment within which biogeochemical and other processes transform materials, including naturally occurring nutrients and human-made contaminants that may sustain or harm the region’s living resources. Thus, understanding and modeling the transport and distribution of materials provides a crucial substrate for determining the effects of biological, geological, and chemical processes. The wide range of scales in which these physical processes operate includes microscale droplets and bubbles; small-scale turbulence in buoyant plumes and the near-surface “mixed” layer; submesoscale fronts, convergent and divergent flows, and small eddies; larger mesoscale quasi-geostrophic eddies; and the overall large-scale circulation of the Gulf of Mexico and its interaction with the Atlantic Ocean and the Caribbean Sea; along with air-sea interaction on longer timescales. The circulation and mixing processes that operate near the Gulf of Mexico coasts, where most human activities occur, are strongly affected by wind- and river-induced currents and are further modified by the area’s complex topography. Gulf of Mexico physical processes are also characterized by strong linkages between coastal/shelf and deeper offshore waters that determine connectivity to the basin’s interior. This physical connectivity influences the transport of materials among different coastal areas within the Gulf of Mexico and can extend to adjacent basins. Major advances enabled by the Gulf of Mexico Research Initiative in the observation, understanding, and modeling of all of these aspects of the Gulf’s physical environment are summarized in this article, and key priorities for future work are also identified.

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