Specialized collection of 0 free data visualization SVG illustrations from the technology & electronics category. Data visualization illustrations including bar charts, network graphs, and information graphics Examples include: bar chart, network graph.

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

The scientific illustration software market is experiencing robust growth, driven by the increasing need for high-quality visuals in scientific publications, presentations, and educational materials. The market's expansion is fueled by several key factors: the rising adoption of digital tools within research institutions and universities, the growing complexity of scientific data requiring sophisticated visualization techniques, and the increasing demand for visually engaging content to disseminate research findings effectively to broader audiences. While precise market sizing data is unavailable, a reasonable estimation based on comparable software markets and the reported CAGR would suggest a 2025 market value around $300 million, projecting to over $500 million by 2033. This growth trajectory is likely to continue, driven by the ongoing integration of AI-powered features into scientific illustration software, which streamlines workflows and enhances the creation of complex diagrams and illustrations. Furthermore, the increasing affordability and accessibility of powerful software coupled with cloud-based solutions will further democratize access and fuel wider adoption across diverse scientific disciplines. Despite the positive outlook, certain challenges might hinder the market's growth. High initial costs for premium software packages could pose a barrier for individual researchers and smaller institutions. Moreover, the need for specialized training and a certain level of technical proficiency can limit adoption among users who lack prior experience with vector graphics or specialized scientific illustration tools. However, the growing availability of free or open-source alternatives, along with user-friendly interfaces and comprehensive tutorials, are mitigating these challenges. The market is also witnessing an increasing trend towards collaborative software and the integration of scientific illustration tools into larger research platforms, further enhancing the workflow and fostering greater team synergy. The segmentation of the market across various software categories (e.g., general-purpose vector graphics, specialized tools for chemistry, biology, etc.) reflects the diverse needs of scientific illustration in various fields.

Specialized collection of 0 free growth charts SVG illustrations from the business & finance category. Business growth illustrations featuring line graphs, pie charts, and data visualization elements Examples include: line graph, pie chart.

These are the Python scripts and data files which underpin the visual techniques presented in the conference paper Kelly, C. & Cuffe, P. "Vector Field Approaches for Summary Visualization of Cross-Border Power Exchanges: Comparing Streamlines and Tessellated Hexagonal Diagrams" forthcoming in IEEE 60th Universities' Power Engineering Conference (UPEC 2025)Data files are taken from the ENTSO-E Transparency Platform and redistributed under the CC-BY 4.0 license.https://transparency.entsoe.eu/

Context

When I started exploring how to create interactive maps (using the leaflet() package in R) I come across this free data set (shapefile format) that contains the geographical coordinates (polygons) for all the countries in the world. I thought it would be nice to share this with the Kaggle community.

Content

The .zip folder contains all the necessary files needed for the shapefile data to work properly on your computer. If you are new to using the shapefile format, please see the information provided below:

https://en.wikipedia.org/wiki/Shapefile "The shapefile format stores the data as primitive geometric shapes like points, lines, and polygons. These shapes, together with data attributes that are linked to each shape, create the representation of the geographic data. The term "shapefile" is quite common, but the format consists of a collection of files with a common filename prefix, stored in the same directory. The three mandatory files have filename extensions .shp, .shx, and .dbf. The actual shapefile relates specifically to the .shp file, but alone is incomplete for distribution as the other supporting files are required. "

Acknowledgements

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com.

Vize Island, Russia, Ground-based Vector Magnetic Field Level 2 Data, 1.0 min Time Resolution, Station Code: (VIZ), Station Location: (GEO Latitude 79.3, Longitude 76.5), AARI Network

The Geospatial Analytics Artificial Intelligence market is poised for substantial growth, with an estimated market size of $10,500 million in 2025. This burgeoning sector is projected to expand at a robust Compound Annual Growth Rate (CAGR) of 22% through 2033, reaching an impressive value unit of millions. This significant expansion is primarily fueled by the increasing adoption of AI and machine learning techniques within the geospatial domain, enabling more sophisticated data analysis and actionable insights. Key drivers include the escalating demand for real-time location intelligence across diverse industries such as real estate for site selection and market analysis, sales and marketing for customer segmentation and targeted campaigns, and agriculture for precision farming and yield optimization. Furthermore, the growing need for enhanced situational awareness in transportation and logistics for route optimization and supply chain management, alongside applications in weather forecasting and disaster management, are propelling market growth. The integration of advanced analytics with spatial data allows for the identification of complex patterns, prediction of future trends, and automation of decision-making processes, making geospatial AI an indispensable tool for businesses and governments worldwide. The market is characterized by a dynamic interplay of technological advancements and evolving application needs. The increasing availability of high-resolution satellite imagery and aerial data, coupled with the proliferation of IoT devices generating location-based data, provides a rich foundation for geospatial AI. Trends such as the rise of cloud-based geospatial platforms, the development of sophisticated AI algorithms for image recognition and spatio-temporal analysis, and the growing emphasis on democratizing access to geospatial insights are shaping the market landscape. While the market enjoys strong growth, certain restraints, such as the high cost of implementing advanced AI solutions and a potential shortage of skilled geospatial AI professionals, may temper the pace of adoption in some segments. However, the inherent value proposition of geospatial analytics AI in driving efficiency, innovation, and informed decision-making across sectors like real estate, sales, agriculture, and transportation, alongside the continuous development of more accessible and powerful tools, ensures its sustained and significant expansion in the coming years. This report delves into the burgeoning field of Geospatial Analytics Artificial Intelligence (AI), analyzing its market dynamics, trends, and future trajectory from 2019 to 2033. With a base year of 2025 and a forecast period extending to 2033, this comprehensive study offers an in-depth examination of a market projected to reach multi-million dollar valuations. We will explore the intricate interplay of AI and location-based data, highlighting how sophisticated algorithms are revolutionizing various industries. The report identifies key players, emerging technologies, and critical growth drivers that are shaping this transformative sector. By understanding the challenges and opportunities, stakeholders can strategically position themselves for success in this rapidly evolving landscape.

The view service WMS-ZABAGED® (ZM10 visualization) is public view map service for viewing ZABAGED® data (including altimetry in the form of contour lines). It is on-line dynamic map service, which is published from vector data stored in a database. Hence, it is possible to work with individual layers. The WMS interface provides GetFeatureInfo operation, which enables WMS clients to query for attributes of ZABAGED® features. Cartographic visualization of the ZABAGED® features is based on the Base map CR 1:10,000 and therefore the service can be also used as a base map for creation of thematic maps. The service is intended for viewing from scale circa 1 : 10 000.

White style vector basemap. It does not interfere with your choropleth for clean and undistorted data visualization. This clean style is used in Disaster N

Highly accurate, professionally designed, enterprise-grade maps available worldwide.

Maps that receive 5 million updates on average per day across the globe for reliable navigation and data visualization.

Vector Tile API Use the freshest, daily updated HERE map data through tiles containing vector data and customize the map style to support your user needs.

Personalize your maps Configure the look and feel of your map by changing color, icon size, width, length and other properties of objects such as buildings, land features and roads. Display it all at the desired zoom level.

Pre-rendered map images Pre-rendered map images in multiple styles, such as base and aerial, optimized for various devices and OS’s. Request an image around a specific area, or at a specified location and zoom level.

Map Tile API Display server-rendered, raster 2D map tiles at different zoom levels, display options, views and schemes. Request tiles that highlight congestion and environmental zones.

Built-in fleet maps Integrate maps designed especially for fleet management applications with accentuated country borders and highways, toll roads within congestion charging zones and highway exits along routes.

Truck attributes layer Provide simple visual cues so that areas with truck restrictions are easily identifiable. Display truck restrictions such as height, weight or environmental restrictions on a variety of map styles.

Map Feedback Offer your users the possibility to edit the HERE map or report errors.

VisIt is an Open Source, interactive, scalable, visualization, animation and analysis tool. From Unix, Windows or Mac workstations, users can interactively visualize and analyze data ranging in scale from small (<101 core) desktop-sized projects to large (>105 core) leadership-class computing facility simulation campaigns. Users can quickly generate visualizations, animate them through time, manipulate them with a

variety of operators and mathematical expressions, and save the resulting images and animations for presentations. VisIt contains a rich set of visualization features to enable users to view a wide variety of data including scalar and vector fields defined on two- and three-dimensional (2D and 3D) structured, adaptive and unstructured meshes. Owing to its customizeable plugin design, VisIt is capabable of visualizing data from over 120 different scientific data formats

This article introduces DOBIN, a new approach to select a set of basis vectors tailored for outlier detection. DOBIN has a simple mathematical foundation and can be used as a dimension reduction tool for outlier detection tasks. We demonstrate the effectiveness of DOBIN on an extensive data repository, by comparing the performance of outlier detection methods using DOBIN and other bases. We further illustrate the utility of DOBIN as an outlier visualization tool. The R package dobin implements this basis construction. Supplementary materials for this article are available online.

The Flood Inundation Mapping (FIM) Visualization Deck is a web-based application designed to display and compare flood extent and depth information across various temporal and scenario conditions. It provides a front-end interface for accessing geospatial flood data and interacting with mapped outputs generated from hydraulic modeling.

Core Functions: • Flood Extent Mapping: Visualizes flood extents from modeled scenarios (e.g., 2-year, 10-year, 100-year events) and real-time conditions based on streamflow observations or forecasts. • Flood Depth Visualization: Displays depth rasters over affected areas, derived from hydraulic simulations (e.g., HEC-RAS). • Scenario Comparison: Allows side-by-side viewing of multiple FIM outputs to support calibration or decision analysis. • Layer Management Toolbox: Users can toggle basemaps, adjust layer transparency, load datasets, and control map extents.

Data Inputs: • Precomputed flood inundation extents (raster/tile layers) • Depth grids • Stream gauge metadata • Associated hydraulic model outputs

Technical Stack: • Front-end: Built with JavaScript, primarily using Leaflet.js for interactive map rendering. • Back-end Services: Uses GeoServer to serve raster tiles and vector layers (via WMS/WFS). Uses OGC-compliant services and REST endpoints for data queries. • Data Formats: Raster layers (e.g., GeoTIFF, PNG tiles), vector layers (GeoJSON, shapefiles), elevation models, and model-derived grid outputs. • Database: Integrates with a PostgreSQL/PostGIS backend or similar spatial database for hydrologic and geospatial data management. • Deployment: Hosted via University of Iowa infrastructure, with modular UI elements tied to specific watersheds or study areas.

Intended Use: The application provides a reference and exploratory tool for comparing modeled flood scenarios, visualizing extent and depth data, and interacting with region-specific inundation data products.

3D Geospatial Technologies Market size was valued at USD 7.27 Billion in 2024 and is projected to reach USD 15.89 Billion by 2032, growing at a CAGR of 8.5% from 2026 to 2032.Rising Demand for Smart City Development: Urban planning initiatives increasingly rely on 3D geospatial technologies to map infrastructure, utilities, and land use. Accurate 3D data helps cities improve planning and resource management. This trend is pushing adoption across both public and private sectors.Growing Use in Defense and Security: Defense agencies are using 3D mapping for mission planning, terrain analysis, and surveillance. These applications enhance situational awareness and decision-making. As geopolitical tensions rise, investment in such technologies continues to grow. Increasing Adoption in Real Estate and Construction: Construction firms and property developers use 3D geospatial tools for site analysis, design visualization, and project monitoring. These technologies help reduce errors and improve efficiency. Their growing integration into planning workflows is fueling market growth.

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:
Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023).

Validation:
The final maps were validated using all pixels of the publicly available IACS parcels from the federal states of Brandenburg, Lower Saxony, and North Rhine-Westphalia that were not used for model training. Classes that are underrepresented in these federal states could therefore not be adequately evaluated (e.g., hops and grapevines). We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

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Mailing list

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References:

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719.

Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

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National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

Adenoviral (Ad) vectors show promise as platforms for vaccine applications against infectious diseases including HIV. However, the requirements for eliciting protective neutralizing antibody and cellular immune responses against HIV remain a major challenge. In a novel approach to generate 2F5- and 4E10-like antibodies, we engineered an Ad vector with the HIV membrane proximal ectodomain region (MPER) epitope displayed on the hypervariable region 2 (HVR2) of the viral hexon capsid, instead of expressed as a transgene. The structure and flexibility of MPER epitopes, and the structural context of these epitopes within viral vectors, play important roles in the induced host immune responses. In this regard, understanding the critical factors for epitope presentation would facilitate optimization strategies for developing viral vaccine vectors. Therefore we undertook a cryoEM structural study of this Ad vector, which was previously shown to elicit MPER-specific humoral immune responses. A subnanometer resolution cryoEM structure was analyzed with guided molecular dynamics simulations. Due to the arrangement of hexons within the Ad capsid, there are twelve unique environments for the inserted peptide that lead to a variety of conformations for MPER, including individual α-helices, interacting α-helices, and partially extended forms. This finding is consistent with the known conformational flexibility of MPER. The presence of an extended form, or an induced extended form, is supported by interaction of this vector with the human HIV monoclonal antibody 2F5, which recognizes 14 extended amino acids within MPER. These results demonstrate that the Ad capsid influences epitope structure, flexibility and accessibility, all of which affect the host immune response. In summary, this cryoEM structural study provided a means to visualize an epitope presented on an engineered viral vector and suggested modifications for the next generation of Ad vectors with capsid-incorporated HIV epitopes.

Medical image analysis is critical to biological studies, health research, computer- aided diagnoses, and clinical applications. Recently, deep learning (DL) techniques have achieved remarkable successes in medical image analysis applications. However, these techniques typically require large amounts of annotations to achieve satisfactory performance. Therefore, in this dissertation, we seek to address this critical problem: How can we develop efficient and effective DL algorithms for medical image analysis while reducing annotation efforts? To address this problem, we have outlined two specific aims: (A1) Utilize existing annotations effectively from advanced models; (A2) extract generic knowledge directly from unannotated images.

To achieve the aim (A1): First, we introduce a new data representation called TopoImages, which encodes the local topology of all the image pixels. TopoImages can be complemented with the original images to improve medical image analysis tasks. Second, we propose a new augmentation method, SAMAug-C, that lever- ages the Segment Anything Model (SAM) to augment raw image input and enhance medical image classification. Third, we propose two advanced DL architectures, kCBAC-Net and ConvFormer, to enhance the performance of 2D and 3D medical image segmentation. We also present a gate-regularized network training (GrNT) approach to improve multi-scale fusion in medical image segmentation. To achieve the aim (A2), we propose a novel extension of known Masked Autoencoders (MAEs) for self pre-training, i.e., models pre-trained on the same target dataset, specifically for 3D medical image segmentation.

Scientific visualization is a powerful approach for understanding and analyzing various physical or natural phenomena, such as climate change or chemical reactions. However, the cost of scientific simulations is high when factors like time, ensemble, and multivariate analyses are involved. Additionally, scientists can only afford to sparsely store the simulation outputs (e.g., scalar field data) or visual representations (e.g., streamlines) or visualization images due to limited I/O bandwidths and storage space. Therefore, in this dissertation, we seek to address this critical problem: How can we develop efficient and effective DL algorithms for scientific data generation and compression while reducing simulation and storage costs?

To tackle this problem: First, we propose a DL framework that generates un- steady vector fields data from a set of streamlines. Based on this method, domain scientists only need to store representative streamlines at simulation time and recon- struct vector fields during post-processing. Second, we design a novel DL method that translates scalar fields to vector fields. Using this approach, domain scientists only need to store scalar field data at simulation time and generate vector fields from their scalar field counterparts afterward. Third, we present a new DL approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results.

The view service WMS-ZABAGED® (visualization for orthophoto) is public view map service for viewing ZABAGED® data (including altimetry in the form of contour lines) with Orthophoto of the Czech Republic. It is on-line dynamic map service, which is published from vector data stored in a database. Hence, it is possible to work with individual layers. The WMS interface provides GetFeatureInfo operation, which enables WMS clients to query for attributes of ZABAGED® features. Cartographic visualization of the ZABAGED® features is done with respect to a combination with the Orthophoto of the Czech Republic. Therefore, the service can be used to create thematic orthopfotomaps. Point and line map symbols are in bold colours to stand out in the orthophoto background. Polygon ZABAGED® features are displayed only by an outline without fill, so they do not cover situation on the orthophoto. The service is intended for viewing from scale circa 1 : 10 000.