Zoom Level Research Articles

Development of a new emission reallocation method for industrial sources in China

Abstract. An accurate emission inventory is a crucial part of air pollution management and is essential for air quality modelling. One source in an emission inventory, an industrial source, has been known with high uncertainty in both location and magnitude in China. In this study, a new reallocation method based on blue-roof industrial buildings was developed to replace the conventional method of using population density for the Chinese emission development. The new method utilized the zoom level 14 satellite imagery (i.e. Google®) and processed it based on hue, saturation, and value (HSV) colour classification to derive new spatial surrogates for province-level reallocation, providing more realistic spatial patterns of industrial PM2.5 and NO2 emissions in China. The WRF-CMAQ-based PATH-2016 model system was then applied with the new processed industrial emission input in the MIX inventory to simulate air quality in the Greater Bay Area (GBA) area (formerly called Pearl River Delta, PRD). In the study, significant root mean square error (RMSE) improvement was observed in both summer and winter scenarios in 2015 when compared with the population-based approach. The average RMSE reductions (i.e. 75 stations) of PM2.5 and NO2 were found to be 11 µg m−3 and 3 ppb, respectively. Although the new method for allocating industrial sources did not perform as well as the point- and area-based industrial emissions obtained from the local bottom-up dataset, it still showed a large improvement over the existing population-based method. In conclusion, this research demonstrates that the blue-roof industrial allocation method can effectively identify scattered industrial sources in China and is capable of downscaling the industrial emissions from regional to local levels (i.e. 27 to 3 km resolution), overcoming the technical hurdle of ∼ 10 km resolution from the top-down or bottom-up emission approach under the unified framework of emission calculation.

School-Based Agricultural Education Teachers Competence of Synchronous Online Instruction Tools During the COVID-19 Pandemic

The COVID-19 pandemic forced educators to move course delivery to online learning with little or no advanced training. One of the immediate concerns of the online course delivery was readiness in using the synchronous learning tools available to school districts. Additionally, moving all students to online learning environments presented a challenge for those teachers who knew little about accessibility tools. The purpose of this study was to assess the awareness and self-perceived competence levels of SBAE teachers in South Carolina related to synchronous online instruction and application of accessibility tools during the COVID-19 pandemic. The majority of the teachers indicated they used Google Meet as the institution's synchronous delivery platform. Of those using Google Meet, their ability to use the mobile application to start meetings and use virtual backgrounds was low. The second most used synchronous delivery platform was Zoom. Some of the skill gaps SBAE teachers listed while using Zoom included breakout rooms, the polling feature, and removing unwanted participants. SBAE teachers reported very low competence in using accessibility features provided in Microsoft Word and PowerPoint applications. Recommendations included future in-service training for SBAE teachers in using advanced levels of Zoom, Google Meet, and WebEx features. Also, training for SBAE teachers on the importance of accessibility and the tools provided is warranted. Faculty teaching educational technologies in pre service programs for SBAE teachers are also encouraged to adapt curricula to support these skills.

Super-resolution decision-making tool using deep convolution neural networks for panchromatic images

In this paper, a Deep Convolution Neural Network (CNN) based Super-Resolution (SR) decision-making tool is proposed for the raw panchromatic satellite image. Conventionally, human visual interpretation through spotting recognizable objects and visualizing the patterns at a certain zooming level in terms of sharp edges, blurs, etc., is in practice at the cost of time consumption and expertise knowledge requirement. A 10-layer deep convolutional neural network is carried out in three steps with inspiration from the categorical signature classification. In the first step, the higher the potential interpretive features for defining the pattern are analyzed. The cross-correlation of interpretative features with the trained images is done in the second step. In the final step, whether the SR technique application is inevitable for the image or not rendered is provided with an accuracy of 83.3% at a minimized loss. The proposed method is tested with seven state-of-the-art classifier architectures for the decision-making tool. The proposed method works well with the dataset considered counterfeiting other techniques.

Effect of Ambient Illumination, Screen Resolution and Zoom Level on Performance of Typing on Computers

Objective: This work aims to investigate effect of the ambient illumination, screen resolution and Zoom levels on visual-related task performance. The job used in this research is typing on computers. Speed of typing paragraph and typing quality were defined as the task performance.
 Background: In recent years, computers play a remarkable role in nearly everyone’s daily life. We use computers for various purposes and under a wide range of ambient illumination. High illumination level usually results in some problems. Adjusting screen resolution and zoom level are common solutions contra problems for visibility due to unsuitable illuminations.
 Method: Ambient illuminations were examined 114, 230, 340, 420 and 520 lx. Screen resolution scales were diverse to cover all range of regulate ability pliable by characteristics of the offering used; 768*1024, 720*1280 and 768*1366. Zoom levels was tested 50, 100, 150 and 200%.
 Results: Based on this study, can be found that, the more effective illumination level on average typing time was 340 followed by 420 lx. Average typing time decrease with increasing screen resolution, the minimum average typing time observed at 720*1280 and 168*1366 screen resolution. Change the zoom level of text show significant effect on typing time, which the average typing time decreases with increasing zoom level. The minimum typing time observed at 100 and 150% zoom level. Experimental results for all S/N ratio, mean, and standard deviation (real) response values show that, illumination level, screen resolution and zoom scale are the significant parameters among all controllable factors that influence the avearge typing time. Based on S/N Ratio the optimum parameters was 114 lx illumination level and 768*1024 screen Resolution and 50% zoom level. Based on standard deviation the optimum parameters was 114 lx illumination level and 768*1366 screen Resolution and 150% zoom level. Based on the means the optimum parameters was is 230lx illumination level and 768*1366 screen Resolution and 100% zoom level.
 Conclusion: The provision of suitable illumination, screen resolution and zoom levels that feedback to enhancing the performance of typing performance on computers.
 Application: This study can inform in-computer typing offices and policy makers concerned with human factors and work-study.

Catalogue of representative scales to visualize different coverages in Google Earth

Representative scales were established, based on the zoom level of Google Maps, for generating a catalogue of indicators from the scale-image relationship, into the Google Earth visualization system. It was possible to analyze some functionalities of the visualization system proposed, such as the depth of detail from image resolution and the images return. Therefore, samples of four land use were analyzed. Furthermore, to associate the multilevel mechanism of the visualization in Google Earth, a digital ruler was used as a tool that measures the pixels per inch into the screen. In consequence, it is possible to analyze the changing behaviour from one screen to another. With this, it is feasible to associate a specific mechanism to improve the relationship between the handling of several images covering a limiting territory, and the scales that best represents the themes described. Finally, 893 samples were analyzed in 32 points of the territory, between the coordinates 14° to 33° N and 86° to 119° W. Finally, the error percentage, the variance, and the standard deviation were estimated for determining the variation between the values calculated in different samples.

Kyrix-S: Authoring Scalable Scatterplot Visualizations of Big Data.

Static scatterplots often suffer from the overdraw problem on big datasets where object overlap causes undesirable visual clutter. The use of zooming in scatterplots can help alleviate this problem. With multiple zoom levels, more screen real estate is available, allowing objects to be placed in a less crowded way. We call this type of visualization scalable scatterplot visualizations, or SSV for short. Despite the potential of SSVs, existing systems and toolkits fall short in supporting the authoring of SSVs due to three limitations. First, many systems have limited scalability, assuming that data fits in the memory of one computer. Second, too much developer work, e.g., using custom code to generate mark layouts or render objects, is required. Third, many systems focus on only a small subset of the SSV design space (e.g. supporting a specific type of visual marks). To address these limitations, we have developed Kyrix-S, a system for easy authoring of SSVs at scale. Kyrix-S derives a declarative grammar that enables specification of a variety of SSVs in a few tens of lines of code, based on an existing survey of scatterplot tasks and designs. The declarative grammar is supported by a distributed layout algorithm which automatically places visual marks onto zoom levels. We store data in a multi-node database and use multi-node spatial indexes to achieve interactive browsing of large SSVs. Extensive experiments show that 1) Kyrix-S enables interactive browsing of SSVs of billions of objects, with response times under 500ms and 2) Kyrix-S achieves 4X-9X reduction in specification compared to a state-of-the-art authoring system.

Visual Analytics for Temporal Hypergraph Model Exploration

Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute HYPER-MATRIX, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of interactions for an effective user-driven exploration of hypergraph models. To eliminate demanding context switches and ensure scalability, our matrix-based visualization provides drill-down capabilities across multiple levels of semantic zoom, from an overview of model predictions down to the content. We facilitate a focused analysis of relevant connections and groups based on interactive user-steering for filtering and search tasks, a dynamically modifiable partition hierarchy, various matrix reordering techniques, and interactive model feedback. We evaluate our technique in a case study and through formative evaluation with law enforcement experts using real-world internet forum communication data. The results show that our approach surpasses existing solutions in terms of scalability and applicability, enables the incorporation of domain knowledge, and allows for fast search-space traversal. With the proposed technique, we pave the way for the visual analytics of temporal hypergraphs in a wide variety of domains.

Automatic aircraft detection in very-high-resolution satellite imagery using a YOLOv3-based process

Aircraft detection in remote-sensing images is a fundamental task in civil and military applications. Deep learning techniques to achieve end-to-end object detection have attracted the attention of the Earth observation community. One of the primary factors behind the success of deep learning techniques is the utilized data. Several previous studies focused on designing the network infrastructure. Instead, this study pays more attention to the data. With the increasing number of available public datasets, whether directly employing a large number of instances with great variation will lead to a good performance has become a research topic. The ways in which these object instances are collected differ greatly. For example, the image sizes, object sizes in the training images, and geospatial resolution are varied. Therefore, herein, the factors influencing the detection performance, such as the object size, ground sampling distance, and Google zoom view, are investigated. A you-only-look-once-v3-based detection process is proposed for automatic aircraft detection. A nonmaximum suppression algorithm strategy is applied to filter unreliable and redundant bounding boxes detected in the overlapping image blocks. The model generalization ability under different training data combinations is evaluated in several challenging cases. The results prove that more variety of training instances from a greater variety of zoom levels will result in more false alarms. Instead, more variety in the object sizes under a constant zoom level is welcome. A large range of aircraft sizes (i.e., 7 to 77 m in length in this study) can be detected, with a promising F1 score of 0.98.

Imaging the Subsurface: How Different Visualizations of Cross-sections Affect the Sense of Uncertainty

Geologists struggle to communicate the uncertainty that arise when mapping and interpreting the geological subsurface. Today, open data sharing policies make new value of geological information possible for a broader user group of non-experts. It is crucial to develop standard methods for visualizing uncertainty to increase the usability of geological information. In this study, a web experiment was set up to analyze whether and how different design choices influence the sense of uncertainty. Also, questions about the intuitiveness of symbols were asked. Two-hundred ten participants from different countries completed the experiment, both experts and non-experts in geology. Traditional visualization techniques in geology, like dashed lines, dotted lines and question mark, were tested. In addition, other visualizations were tested, such as hatched area and variations of symbol size, zoom levels and reference information. The results show that design choices have an impact on the participants’ assessment of uncertainty. The experts inquire about crucial information if it is not present. The results also suggest that when visualizing uncertainty, all the elements in the representation, and specifically the line and area symbols that delineate and colour the features, must work together to make the right impression.

Robust control point estimation with an out-of-focus camera calibration pattern

Abstract The calibration of a zoom lens camera depends on the precision of the localization of control points. At a long focal length, the narrow depth-of-field (DOF) causes defocused blurring and an inevitable decrease in accuracy in control points localization. In particular, the camera calibration requires multiple control points defined on calibration patterns acquired at various camera angles. However, clear pattern images are difficult to obtain owing to the narrow DOF. We propose a robust and intuitive method to accurately estimate the control points in blurred images. To obtain control points that are less affected by blurring, we dynamically varied the circle size in the patterns and identified the local maximum point using the intensity gradient of accumulated concentric circles. This approach is robust to blurring and can be employed at all zoom levels. In our experiments, the error of the control point estimation was measured while varying the angles of the calibration patterns and the degree of blurring. Compared with the conventional checker pattern method, the performance of the proposed method in the estimation of the control points was better and its related camera parameters with severely defocused images were settled.

Histopathology and Ultrastructural Findings of Fatal COVID-19 Infections on Testis.

PurposeTo evaluate the presence and analyze the pathological changes within the testes of patients who died or recovered from severe acute respiratory syndrome coronavirus 2 (COVID-19) complications.Materials and MethodsTestis tissue was collected from autopsies of COVID-19 positive (n=6) and negative men (n=3). Formalin-fixed paraffin-embedded tissues were stained with hematoxylin and eosin (H&E) and subjected to immunofluorescence for angiotensin-converting enzyme 2 (ACE-2) expression. Fluorescent-labeled tissue slides were imaged on a quantitative pathology scope with various zoom levels allowing for qualitative and quantitative interpretation. Tissue from four COVID-19 positive autopsy cases and a live seroconverted patient was imaged with transmission electron microscopy (TEM).ResultsH&E histomorphology showed three of the six COVID-19 biopsies had normal spermatogenesis while the remaining three had impaired spermatogenesis. TEM showed the COVID-19 virus in testis tissue of one COVID-19 positive autopsy case and the live biopsy, H&E stain on the same autopsy case demonstrated interstitial macrophage and leukocyte infiltration. Immunofluorescent stained slides from six COVID-19 positive men demonstrated a direct association between increased quantitative ACE-2 levels and impairment of spermatogenesis.ConclusionsThe novel COVID-19 has an affinity for ACE-2 receptors. Since ACE-2 receptor expression is high in the testes, we hypothesized that COVID-19 is prevalent in testes tissue of infected patients. This study suggests the male reproductive tract, specifically the testes, may be targets of COVID-19 infection. We found an inverse association between ACE-2 receptor levels and spermatogenesis, suggesting a possible mechanism of how COVID-19 can cause infertility.

A new web map for the MeinGrün app

Abstract. The starting point for developing a new map was defined by the requirements of the “MeinGrün” project (can be translated as ‘my green’ project). The aim is the development of a mobile app, which enables routing to urban areas, including suggestions for attractive places for different activities. A specific map style was designed to communicate information related to green spaces effectively and to make users excited about the topic of the project.Among other reasons, we also want to apply our knowledge on vector tile generation and use it in teaching. For the demonstration of our research on generalization, it is beneficial to offer a web map service because such a service allows illustrating developed methods and results. Finally, the experience enables us to train students in the design and development of customized styles for zoomable web maps.The aim is to build a map service with several zoom levels up to small scales using data from OpenStreetMap and NaturalEarth. For better adaptivity, it is aimed to offer the map as vector and raster tiles with the same style. Besides, making high-resolution raster tiles available for retina displays is considered as well. The rationale is that everyone can choose the appropriate method of delivery for different contexts of application. These requirements result in some technological challenges: provide vector and raster tiles at the same time and style, keep the computation time for an update, and generation low. For providing and delivering up to date data, on-the-fly map generalization has been implemented, e.g., scale-dependent selection, simplification, and aggregation of various features.Figure 1 shows our result, a map in warm colors, and a reduced information load for serving a reference map. The visualization of roads with a simple line is unique, but this was also the intension of the applied research project: the production of something unique and easy rememberable for the “MeinGrün” project. The hill-shading uses raster tiles while all other map features work with vector tiles.The data transformation from the OpenStreetMap format to the spatial PostgreSQL/PostGIS database has been applied with IMPOSM, a well-documented and efficient tool. GDAL is used to import the NaturalEarth data and doing some processing steps. PostgreSQL and its spatial extension PostGIS serve in many web-mapping projects as a data store and offer a wide range of options for processing data. After specified queries are applied, the t_rex software generates the vector tiles from the database, which are then packed as MBtiles using mbutil. Finally, Tileserver GL is used to serve vector and raster tiles, including further necessary additions such as style, sprites, and glyphs.To sum up, we already have a working service, which is available on our server with an ongoing blog about the project. Still, it is a work on progress project that needs further research and development. For example, the integration of Wikipedia/Wikidata information for selecting place by their importance, transliteration of no-Latin place names for a world-wide coverage, and more and better labeling of features. Now the map extent is scale depended and covers at low zoom levels the whole world. In contrast, the higher zoom levels are only available in Dresden and Heidelberg to match the requirements of the “MeinGrün” project but will be extended in the future.

An Improved Method to Detect Shoreline Changes in Small-Scale Beaches Using Google Earth Pro

Shoreline change studies in small-scale beaches require high-resolution satellite images. In this regard, high-resolution satellite images from Google Earth (GE) would be an alternative source however novel studies are needed to verify the effectiveness and the efficiency of applying those images for shoreline change detection in small-scale beaches. Addressing this gap, the current study attempts to develop a new method. Accuracies of delineated shorelines under different scenarios were used to develop relationships with digitizing methods and used eye-altitude to estimate the most effective, efficient and productive method. This was done using Digital Shoreline Analysis System (DSAS) in ArcGIS software. It was found that the eye-altitude influences on digitizing accuracy and it could be improved when increasing the zoom level of the image which is under investigation. Maximum zoom level (50 m) used in this study showed the highest accuracy in shoreline digitizing while the most productive eye-altitude for shoreline delineation was found as 300 m. The current study identified that GE coupled with DSAS tool in ArcGIS software can be used as an effective and efficient method for small-scale shoreline change analysis and it is suggested that this methodology could be adopted for other similar studies.

Multi-scale optical coherence tomography imaging and visualization of Vermeer's Girl with a Pearl Earring.

We demonstrate multi-scale multi-parameter optical coherence tomography (OCT) imaging and visualization of Johannes Vermeer's painting Girl with a Pearl Earring. Through automated acquisition, OCT image segmentation, and 3D volume stitching we realize OCT imaging at the scale of an entire painting. This makes it possible to image, with micrometer axial and lateral resolution, an entire painting over more than 5 orders of length scale. From the multi-scale OCT data we quantify multiple parameters in a fully automated way: the surface height, the scattering strength, and the combined glaze and varnish layer thickness. The multi-parameter OCT data of Girl with a Pearl Earring shows various features: Vermeer's brushstrokes, surface craquelure, paint losses, and restorations. Through an interactive visualization of the Girl, based on the OCT data and the optical properties of historical reconstructions of Vermeer's paint, we can virtually study the effect of the lighting condition, viewing angle, zoom level and presence/absence of glaze layer. The interactive visualization shows various new painting features. It demonstrates that the glaze layer structure and its optical properties were essential to Vermeer to create an extremely strong light to dark contrast between the figure and the background that gives the painting such an iconic aesthetic appeal.

LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training

Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2,067,710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Fréchet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets.

Hydrodynamic plastic flow in metal materials

The band structures (microbands and shearbands) were first discovered and attracted attention of researchers in connection with plastic flow localization and absence of dislocations sliding signs during deformation. Experimental investigations of structure formations during plastic deformation conducted in subsequent 70 years did not make clear the nature of these bands. Nevertheless the study of structure formation in various metals and alloys at different means of deformation revealed the bands to arise in chaotic ensemble of dislocations after destruction of previous cell structure, to become on all zoom levels and to localize the deformation. Taking into account the plastic deformation to pass far from thermodynamic equilibrium we proposed the synergetic model of these structures formation in metallic crystals. It was revealed the band structures to secure the possibility of plastic deformation without dislocations by hydrodynamic plastic flow.

Foveation Pipeline for 360° Video-Based Telemedicine.

Pan-tilt-zoom (PTZ) and omnidirectional cameras serve as a video-mediated communication interface for telemedicine. Most cases use either PTZ or omnidirectional cameras exclusively; even when used together, images from the two are shown separately on 2D displays. Conventional foveated imaging techniques may offer a solution for exploiting the benefits of both cameras, i.e., the high resolution of the PTZ camera and the wide field-of-view of the omnidirectional camera, but displaying the unified image on a 2D display would reduce the benefit of “omni-” directionality. In this paper, we introduce a foveated imaging pipeline designed to support virtual reality head-mounted displays (HMDs). The pipeline consists of two parallel processes: one for estimating parameters for the integration of the two images and another for rendering images in real time. A control mechanism for placing the foveal region (i.e., high-resolution area) in the scene and zooming is also proposed. Our evaluations showed that the proposed pipeline achieved, on average, 17 frames per second when rendering the foveated view on an HMD, and showed angular resolution improvement on the foveal region compared with the omnidirectional camera view. However, the improvement was less significant when the zoom level was 8× and more. We discuss possible improvement points and future research directions.

AgentVis: Visual Analysis of Agent Behavior With Hierarchical Glyphs.

Glyphs representing complex behavior provide a useful and common means of visualizing multivariate data. However, due to their complex shape, overlapping, and occlusion of glyphs is a common and prominent limitation. This limits the number of discreet data tuples that can be displayed in a given image. Using a real-world application, glyphs are used to depict agent behavior in a call center. However, many call centers feature thousands of agents. A standard approach representing thousands of agents with glyphs does not scale. To accommodate the visualization incorporating thousands of glyphs we develop clustering of overlapping glyphs into a single parent glyph. This hierarchical glyph represents the mean value of all child agent glyphs, removing overlap and reduTcing visual clutter. Multi-variate clustering techniques are explored and developed in collaboration with domain experts in the call center industry. We implement dynamic control of glyph clusters according to zoom level and customized distance metrics, to utilize image space with reduced overplotting and cluttering. We demonstrate our technique with examples and a usage scenario using real-world call-center data to visualize thousands of call center agents, revealing insight into their behavior and reporting feedback from expert call-center analysts.

Digital revolution: the experience of periodization and problems of forecasting

This article examines a complicated phenomenon that includes the epistemic and technological components, as well as multiplicity of social practices. Based on such approach, the author determines the significant stages of “digitalization” of various sides of social life; describes the problems impeding accurate forecasting of the processes of future development of information and communication technologies and their impact upon social life. Methodological patterns that should be followed by a researcher aimed at implementation of a forecasting function with regards to future transformations of sociotechnical digital world. Methodological framework contains the historical-comparative analysis that allows identifying problematic areas in the attempts to establish periodization of the development of computer science and desire to form forecast of the future flow of transformations in the “digital world”. Bases on the concepts “open exit project” and “medium zoom level”, the article reveals the capabilities and limitations for accurate realization of forecast functions with regards to further course of the “digital revolution”. The author substantiates the statement that in studying processes associated with future development of computer science, it is necessary to pay attention to socio-anthropological consequences of research and engineering activity.

Performance Testing on Vector vs. Raster Map Tiles—Comparative Study on Load Metrics

Recent developments in web map applications have widely affected how background maps are rendered. Raster tiles are currently considered as a regular solution, while the use of vector tiles is becoming more widespread. This article describes an experiment to test both raster and vector tile methods. The concept behind raster tiles is based on pre-generating an original dataset including a customized symbology and style. All tiles are generated according to a standardized scheme. This method has a few disadvantages: if any change in the dataset is required, the entire tile-generating process must be redone. Vector tiles manipulate vector objects. Only vector geometry is stored on the server, while symbology, rendering, and defining zoom levels run on the client-side. This method simplifies changing symbology or topology. Based on eight pilot studies, performance testing on loading time, data size, and the number of requests were performed. The observed results provide a comprehensive comparison according to specific interactions. More data, but only one or two tiles, were downloaded for vector tiles in zoom and move interactions, while 40 tiles were downloaded for raster tiles for the same interactions. Generally, the WebP format downloaded about three times fewer data than Portable Network Graphics (PNG).