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Category: INFEWS Research

In bold letters "NSF"

The Perception of Graph Properties in Graph Layouts

When looking at drawings of graphs, questions about graph density, community structures, local clustering and other graph properties may be of critical importance for analysis. While graph layout algorithms have focused on minimizing edge crossing, symmetry, and other such layout properties, there is not much known about how these algorithms relate to a user’s ability to perceive graph properties for a given graph layout.

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Urban form and composition of street canyons: A human-centric big data and deep learning approach

Various research applications require detailed metrics to describe the form and composition of cities at fine scales, but the parameter computation remains a challenge due to limited data availability, quality, and processing capabilities. We developed an innovative big data approach to derive street-level morphology and urban feature composition as experienced by a pedestrian from Google Street View (GSV) imagery.

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Sky View Factor footprints for urban climate modeling

Urban morphology is an important multidimensional variable to consider in climate modeling and observations, because it significantly drives the local and micro-scale climatic variability in cities. Urban form can be described through urban canopy parameters (UCPs) that resolve the spatial heterogeneity of cities by specifying the 3-dimensional geometry, arrangement, and materials of urban features.

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A boosted decision tree approach to shadow detection in scanning electron microscope (SEM) images for machine vision applications

Scanning electron microscopy is important across a wide range of machine vision applications, and the ability to detect shadows in images could provide an important tool for evaluating attributes of the surfaces being imaged, such as the presence of defects or particulate impurities. One example where the presence of shadows can be important is in the reconstruction of elevation maps from stereo-pair scanning electron microscopy (SEM) images.

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In bold letters "NSF"

Advancing water science through community collaboration

The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) is an independent non-profit in the United States that is funded by the US National Science Foundation and overseen by a board of directors elected from university membership in the USA. In its mission statement, it sets out goals to support the community to advance water science and to improve societal well-being by developing, supporting, and operating research infrastructure, by improving access to data, information and models, by articulating priorities for community level water-related research and observations, by facilitating interactions among the diverse water research community, by promoting interdisciplinary education centered in water science, and by translating scientific advancements into effective tools for water management.

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In bold letters "NSF"

Improving the representation of hydrologic processes in Earth System Models

Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river basin, continent, and global scales. However, current large‐scale land models (as a component of Earth System Models, or ESMs) do not yet reflect the best hydrologic process understanding or utilize the large amount of hydrologic observations for model testing.

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