Electronic maps (E-maps) provide people with convenience in real-world space. Although web map services can display maps on screens, a more important function is their ability to access geographical features. An E-map that is based on raster tiles is inferior to vector tiles in terms of interactive ability because vector maps provide a convenient and effective method to access and manipulate web map features. However, the critical issue regarding rendering tiled vector maps is that geographical features that are rendered in the form of map symbols via vector tiles may cause visual discontinuities, such as graphic conflicts and losses of data around the borders of tiles, which likely represent the main obstacles to exploring vector map tiles on the web. This paper proposes a tiled vector data model for geographical features in symbolized maps that considers the relationships among geographical features, symbol representations and map renderings. This model presents a method to tailor geographical features in terms of map symbols and ‘addition’ (join) operations on the following two levels: geographical features and map features. Thus, these maps can resolve the visual discontinuity problem based on the proposed model without weakening the interactivity of vector maps. The proposed model is validated by two map data sets, and the results demonstrate that the rendered (symbolized) web maps present smooth visual continuity.

The lidar 10m Vector Ruggedness Measure is the primary 10m Vector Ruggedness Measure data product produced and distributed by the National Park Service, Great Smoky Mountains National Park.

Time cost analysis of generating tiles for the three models (in seconds).

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) 📍

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

Time cost analysis of rendering tiles for the three models (in seconds).

The global Vector Database Software market is poised for substantial growth, projected to reach an estimated $XXX million in 2025, with an impressive Compound Annual Growth Rate (CAGR) of XX% during the forecast period of 2025-2033. This rapid expansion is fueled by the increasing adoption of AI and machine learning across industries, necessitating efficient storage and retrieval of unstructured data like images, audio, and text. The burgeoning demand for enhanced search capabilities, personalized recommendations, and advanced anomaly detection is driving the market forward. Key market drivers include the widespread implementation of large language models (LLMs), the growing need for semantic search functionalities, and the continuous innovation in AI-powered applications. The market is segmenting into applications catering to both Small and Medium-sized Enterprises (SMEs) and Large Enterprises, with a clear shift towards Cloud-based solutions owing to their scalability, cost-effectiveness, and ease of deployment. The vector database landscape is characterized by dynamic innovation and fierce competition, with prominent players like Pinecone, Weaviate, Supabase, and Zilliz Cloud leading the charge. Emerging trends such as the development of hybrid search capabilities, integration with existing data infrastructure, and enhanced security features are shaping the market's trajectory. While the market shows immense promise, certain restraints, including the complexity of data integration and the need for specialized technical expertise, may pose challenges. Geographically, North America is expected to dominate the market share due to its early adoption of AI technologies and robust R&D investments, followed closely by Asia Pacific, which is witnessing rapid digital transformation and a surge in AI startups. Europe and other emerging regions are also anticipated to contribute significantly to market growth as AI adoption becomes more widespread. This report delves into the rapidly evolving Vector Database Software Market, providing a detailed analysis of its landscape from 2019 to 2033. With a Base Year of 2025, the report offers crucial insights for the Estimated Year of 2025 and projects market dynamics through the Forecast Period of 2025-2033, building upon the Historical Period of 2019-2024. The global vector database software market is poised for significant expansion, with an estimated market size projected to reach hundreds of millions of dollars by 2025, and anticipated to grow exponentially in the coming years. This growth is fueled by the increasing adoption of AI and machine learning across various industries, necessitating efficient storage and retrieval of high-dimensional vector data.

This dataset contains the statistical Units of the Netherlands according to the INSPIRE data model for Statistical Units version 3.0 It contains the following SU-types: neighborhood, district, municipality, province, part of the country, nuts1, nuts2, nuts3, corop area and gdregion. You can filter them out with the StatisticalTessellation attribute, as long as the SU-vector data model has no SU-Type attribute.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

• CORINE Land Cover (CLC) database
• Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/
• Extract from Terms of Use:

Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:

• When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.

• Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.

• Where that data or information has been adapted or modified, the user shall clearly state this.

• The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.

• https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

eez/*

• World exclusive economic zones (EEZ)
• Source: http://www.marineregions.org/sources.php#unioneezcountry
• Extract from Terms of Use:

Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.

• http://www.marineregions.org/disclaimer.php

natura/*

• Natura 2000 natural protection areas
• Source: https://www.eea.europa.eu/data-and-maps/data/natura-10
• Extract from Terms of Use:

EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: Directorate-General for Environment (DG ENV).

• https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

naturalearth/*

• World country shapes
• Source: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/
• Extract from Terms of Use:

All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.

No permission is needed to use Natural Earth. Crediting the authors is unnecessary.

• http://www.naturalearthdata.com/about/terms-of-use/

NUTS_2013_60M_SH/*

• Europe NUTS3 regions
• Source: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• Extract from Terms of Use:

In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:

1. The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").

2. The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.

• https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• https://ec.europa.eu/eurostat/about/policies/copyright

gebco/GEBCO_2014_2D.nc

• GEBCO bathymetric dataset
• Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/
• Extract from Terms of Use:

The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid indicates that the user accepts the conditions of use and disclaimer information given below.

Users are free to:

• Copy, publish, distribute and transmit The GEBCO Grid

• Adapt The GEBCO Grid

• Commercially exploit The GEBCO Grid, by, for example, combining it with other information, or by including it in their own product or application

Users must:

• Acknowledge the source of The GEBCO Grid. A suitable form of attribution is given in the documentation that accompanies The GEBCO Grid.

• Not use The GEBCO Grid in a way that suggests any official status or that GEBCO, or the IHO or IOC, endorses any particular application of The GEBCO Grid.

• Not mislead others or misrepresent The GEBCO Grid or its source.

• https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf

je-e-21.03.02.xls

• Population and GDP data for Swiss Cantons
• Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html
• Extract from Terms of Use:

Information on the websites of the Federal Authorities is accessible to the public. Downloading, copying or integrating content (texts, tables, graphics, maps, photos or any other data) does not entail any transfer of rights to the content.

Copyright and any other rights relating to content available on the websites of the Federal Authorities are the exclusive property of the Federal Authorities or of any other expressly mentioned owners.

Any reproduction requires the prior written consent of the copyright holder. The source of the content (statistical results) should always be given.

This digital publication, GPR 2008-1, contains geophysical data and a digital elevation model that were produced from airborne geophysical surveys conducted in 2007 for part of the western Fortymile mining district, east-central Alaska. Aeromagnetic and electromagnetic data were acquired for 250 sq miles during the helicopter-based survey. Data provided in GPR 2008-1 include processed (1) linedata ASCII database, (2) gridded files of magnetic data, a calculated vertical magnetic gradient (first vertical derivative), apparent resistivity data, and a digital elevation model, (3) vector files of data contours and flight lines, and (4) the Contractor's descriptive project report. Data are described in more detail in the "GPR2008-1Readme.pdf" and "linedata/GPR2008-1-Linedata.txt" files included on the DVD.

Snapshot img { margin: 10px !important; } Vector Database Market Size 2025-2029

The vector database market size is forecast to increase by USD 2.1 billion, at a CAGR of 14.7% between 2024 and 2029.

The global vector database market is expanding, primarily driven by the proliferation of generative AI and large language models. These advanced AI systems rely on vector embeddings for semantic understanding, making vector databases a critical infrastructure component. The strategic integration of vector search into existing data platforms is a defining trend, simplifying the data stack for enterprises. This move toward a unified approach for ai data management, often within a hyperscale data center environment, allows for powerful hybrid searches. However, the complexity of implementation and a lack of skilled talent present a significant challenge to widespread adoption, requiring a rare blend of expertise in data science and specialized database administration.This skills gap creates a high barrier to entry, particularly for small and medium-sized enterprises. The intricate process involves selecting an appropriate embedding model, generating high-quality vectors, and fine-tuning numerous parameters. Managing this machine learning pipeline, which may involve gpu database acceleration or synthetic data generation, demands specialized knowledge. As organizations increasingly look to build their own autonomous data platform, the need for user-friendly managed services and automated tuning features becomes paramount. Mitigating this complexity is key for companies to effectively leverage vector databases for advanced data analytics and build competitive AI-powered applications without significant project delays or suboptimal performance.

What will be the Size of the Vector Database Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe ongoing evolution of the global vector database market is characterized by a continuous refinement of indexing algorithms and data management techniques. Innovations in on-disk indexing are making large-scale similarity search more accessible, moving beyond the limitations of purely in-memory solutions. This shift is critical for enabling complex data analytics and search applications that rely on semantic understanding of massive datasets. The development of hybrid search capabilities, combining scalar and vector queries, is also becoming a standard expectation for modern data platforms.Furthermore, the market's dynamism is evident in the push toward serverless architectures and managed services. These models are lowering the barrier to entry for enterprises seeking to deploy AI-powered features without the operational overhead of self-hosting. This trend aligns with the broader industry move to cloud-native development and is fostering faster innovation cycles. The demand for efficient, scalable, and cost-effective solutions for ai data management continues to shape the competitive landscape, driving both pure-play specialists and established platform providers to enhance their offerings in the in-memory data grid market.

How is this Vector Database Industry segmented?

The vector database industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. DeploymentOn-premisesCloud-basedHybridApplicationNLPImage and video recognitionRecommendation systemsFraud detectionEnd-userIT and telecommunicationsBFSIRetail and e-commerceHealthcareOthersGeographyNorth AmericaUSCanadaMexicoAPACChinaJapanIndiaSouth KoreaAustraliaIndonesiaEuropeGermanyUKFranceThe NetherlandsItalySpainMiddle East and AfricaUAESouth AfricaTurkeySouth AmericaBrazilArgentinaColombiaRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The on-premises deployment model remains a critical component of the global vector database market, catering to organizations with stringent requirements for data security, governance, and regulatory compliance. Industries such as finance and healthcare often mandate that sensitive data, including proprietary vector embeddings, remain within their physical control. This model offers maximum control over the data environment and can provide ultra-low latency for applications. On-premises deployments currently account for approximately 62% of the market's value.This deployment model is also favored by enterprises that have made significant prior investments in private data center infrastructure. They prefer to leverage existing hardware and operational expertise to manage their AI workloads. While this approach involves higher upfront capital expenditu

Vector Database as a Service Market Outlook

According to our latest research, the global Vector Database as a Service market size stood at USD 1.21 billion in 2024, reflecting the rapidly increasing adoption of AI-driven technologies and the need for efficient data retrieval systems. The market is set to grow at a robust CAGR of 27.8% during the forecast period, reaching an estimated USD 10.45 billion by 2033. This exceptional growth is primarily fueled by the proliferation of artificial intelligence (AI) and machine learning (ML) applications, which require advanced vector databases for high-speed similarity search and large-scale data processing.

The surging demand for AI-powered applications across industries is a critical growth factor for the vector database as a service market. As organizations increasingly deploy AI and ML models for tasks such as recommendation engines, natural language processing, and computer vision, the need for specialized databases that can efficiently handle vectorized data has become paramount. Vector databases enable rapid similarity searches and real-time analytics on unstructured data, which are essential capabilities for modern enterprise applications. Furthermore, the shift towards cloud-based infrastructure is accelerating the adoption of vector database as a service solutions, as organizations seek scalable, cost-effective, and easily deployable platforms to support their digital transformation initiatives.

Another significant driver for the vector database as a service market is the exponential growth of unstructured data, such as images, videos, and textual content, generated by businesses and consumers alike. Traditional relational databases are ill-equipped to manage and analyze such data at scale, prompting organizations to adopt vector databases that can store, index, and retrieve high-dimensional vectors efficiently. This capability is particularly valuable in sectors like e-commerce, media, and healthcare, where real-time personalization, semantic search, and anomaly detection are becoming standard requirements. The integration of vector databases with existing data ecosystems, including data lakes and warehouses, further enhances their value proposition, making them a critical component of next-generation data architectures.

The increasing emphasis on data-driven decision-making and the rise of sophisticated fraud detection and cybersecurity solutions are also propelling the growth of the vector database as a service market. Financial institutions, healthcare providers, and e-commerce platforms are leveraging advanced vector search capabilities to identify anomalies, detect fraud, and enhance the accuracy of predictive analytics. As regulatory requirements around data privacy and security become more stringent, the demand for secure, compliant, and easily managed database services is rising. Vendors offering robust security features, compliance certifications, and seamless integration with enterprise IT environments are gaining a competitive edge in this rapidly evolving market.

Vector Search is becoming increasingly vital in the realm of data management, particularly as enterprises strive to enhance the accuracy and efficiency of their AI-driven applications. This advanced search method allows for the rapid retrieval of data by evaluating the similarity between high-dimensional vectors, which is crucial for applications like recommendation systems and image recognition. As organizations handle larger and more complex datasets, the ability to perform vector search efficiently can significantly impact the performance and scalability of their AI models. By integrating vector search capabilities, businesses can achieve faster data processing times and more precise results, ultimately driving better decision-making and customer experiences.

From a regional perspective, North America currently dominates the vector database as a service market, driven by the presence of leading technology companies, high investment in AI research, and the early adoption of cloud-based solutions. However, Asia Pacific is expected to exhibit the highest growth rate during the forecast period, fueled by rapid digitalization, the proliferation of AI startups, and increasing government initiatives to promote advanced data analytics. Europe, Latin America, and the Middle East & Africa are also witnessing gr

Building model (LOD0.4) Basic data from the area multipurpose map (FMZK) - the digital city map of Vienna - as vector data: https://www.wien.gv.at/urban development/urban survey/geodaten/fmzk/index.html More information on the data status in the road area and in the interior of the road blocks can be found in the data set.Multipurpose map sheet information 1000 Vienna. https:///www.data.gv.at/katalog/dataset/b2d17060-b2f4-4cd7-a2e5-64beccfeb4c1

A necessary component of understanding vector-borne disease risk is the accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data-rich species but may produce inaccurate results for sparsely-documented vectors. In light of global change, vectors that are currently not well-documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a sparsely-documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely-documented ..., Vector Data Vector presence data were obtained from VectorMap and iNaturalist. Only iNaturalist data considered “research grade†were included, and we removed duplicates. To obtain absence data, we referenced VectorMap publications and assumed that if a species was not reported at a sampling location, but was included within the study, that the species was absent at that location. To avoid conflating low sampling effort with low vector presence, we based pseudo-absence locations on presence locations from chiggers, fleas, and mites from both databases and the Global Biodiversity Information Facility. We used a 1:1 ratio of presence to absence points, which produces the most accurate predicted distribution for regression techniques (Barbet-Massin et al., 2012). We artificially sparsely sampled one species within our empirical data (A. maculatum) by including 20% of available presence-absence data in our training set and withholding the rest for testing. The artificial sparse sampling all..., , # Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling

https://doi.org/10.5061/dryad.jwstqjqhq

Description of the data and file structure

• vector_data.csv: presence/absence locations of vectors with corresponding scaled environmental covariates (please see methods for sources for covariate data).
• FL_case_data.xlsx: Human disease data. Sheets 1-3 include annual county-level count data for each disease. Sheets 4 -6 include county-level presence data for each disease. The order of counties and years reported for each disease are given in the "meta" sheet.Â
• distance_to_pathogen_fl.xlsx: includes data on the number of cases reported in pets within each county over the study period, and whether the binary classification for circulation. d_circ is the distance to the closest county in which the disease is circulating (based on the county midpoint).Â
• ...

Vector Database as a Service Market Outlook

According to our latest research, the global Vector Database as a Service (DBaaS) market size reached USD 1.12 billion in 2024, driven by surging demand for AI-powered applications and data-intensive workloads. The market is expected to grow at a robust CAGR of 27.4% from 2025 to 2033, with the market size projected to reach USD 9.41 billion by 2033. This remarkable growth is primarily fueled by the increasing adoption of machine learning, generative AI, and advanced semantic search technologies across industries, as organizations seek scalable, real-time data solutions to power next-generation applications.

The primary growth factor for the Vector Database as a Service market is the exponential rise in unstructured and high-dimensional data generated by enterprises. Organizations across sectors such as BFSI, healthcare, e-commerce, and telecommunications are increasingly leveraging AI-driven applications that require rapid, accurate similarity search and retrieval from massive datasets. Traditional relational databases are ill-suited for these workloads, prompting a shift toward vector databases that can handle embeddings and facilitate real-time semantic search. This technological shift is further amplified by the proliferation of large language models (LLMs) and generative AI, both of which inherently depend on vector representations and require robust, scalable vector data infrastructure.

Another significant driver is the growing adoption of cloud-based solutions and managed services. Enterprises are rapidly moving away from on-premises database management due to the high costs, complexity, and lack of scalability associated with traditional systems. Vector Database as a Service enables organizations to deploy, scale, and manage high-performance vector databases with minimal operational overhead, allowing them to focus on core business and innovation. The pay-as-you-go pricing models and seamless integration with cloud-native AI/ML workflows further enhance the appeal of DBaaS offerings. This trend is particularly pronounced among small and medium enterprises (SMEs) that lack the resources for in-house data infrastructure but require advanced capabilities to stay competitive.

The increasing focus on personalized user experiences and intelligent automation is also propelling the Vector Database as a Service market. Recommendation engines, semantic search platforms, fraud detection systems, and advanced analytics all rely on the ability to process and analyze high-dimensional vectors in real time. As organizations strive to deliver hyper-personalized content and services, the need for scalable, low-latency vector search capabilities becomes paramount. This demand is further bolstered by the rise of hybrid and multi-cloud environments, where DBaaS solutions offer flexibility, reliability, and seamless integration across diverse IT landscapes. As a result, the market is witnessing heightened investment from both established technology vendors and innovative startups aiming to capture a share of this rapidly expanding landscape.

Regionally, North America remains at the forefront of the Vector Database as a Service market, accounting for the largest share in 2024 due to the early adoption of AI technologies, strong presence of leading cloud providers, and a mature digital ecosystem. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, burgeoning AI research, and increasing investments in cloud infrastructure. Europe is also witnessing significant growth, supported by stringent data regulations and a growing focus on enterprise AI adoption. Latin America and the Middle East & Africa are gradually catching up, with local enterprises and governments recognizing the value of advanced vector data solutions for economic modernization and digital competitiveness.

Offering Analysis

The Vector Database as a Service market is segmented by offering into Solutions and Services. Solutions encompass the core vector database platforms, APIs, and software tools that enable organizations to store, index, and search high-dimensional vectors at scale. These offerings are rapidly evolving to support advanced features such as hybrid search (combining vector and keyword search), real-time analytics, and integration with popular AI/ML frameworks. As enterprises increasingly demand seamless, end-to-end data pipelin

If you are interested in obtaining a copy of this data, see LIO Support - Large Data Ordering Instructions. Data can be requested by project area or a set of tiles. To determine which project contains your area of interest or to view single tiles, zoom in on the map above and click. For bulk tile orders follow the link in the Additional Documentation section below to download the tile index in shapefile format. Data sizes by project area are listed below. Data sizes are listed below.

The Ontario Classified Point Cloud (Imagery-Derived) is a classified elevation point cloud based on aerial photography. The point cloud has been classified into Unclassified, Ground and Noise categories and is structured in non-overlapping 1-km by 1-km tiles in a compressed format. For more details about the product see the User Guides linked below.

Raster derivatives have been created from the point clouds for some imagery projects. These products may meet your needs and are available for direct download. See the Ontario Digital Elevation Model (Imagery-Derived) for a representation of bare earth and the Ontario Digital Surface Model (Imagery-Derived) for a model representing all surface features.

Additional Documentation

Ontario Classified Point Cloud (Imagery-Derived) - User Guide (DOCX)

Ontario Classified Point Cloud (Imagery-Derived) - Tile Index (SHP)

Data Package Sizes

SWOOP 2010 - 826 GB SCOOP 2013 - 118 GB DRAPE 2014 - 114 GBSWOOP 2015 - 112 GB COOP 2016 - 45.8 GB NWOOP 2017 - 126 GB

Status On going: Data is continually being updated

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical processes, mobile communication channels and biomedical signals. In the literature, most work utilize multivariate Gaussian models for the mentioned applications, mainly due to the lack of efficient analytical tools for modeling with non-Gaussian distributions. In this paper, we propose a particle filtering approach which can model non-Gaussian autoregressive processes having cross-correlations among them. Moreover, time-varying parameters of the process can be modeled as the most general case by using this sequential Bayesian estimation method. Simulation results justify the performance of the proposed technique, which potentially can model also Gaussian processes as a sub-case.

IntroductionSpecies distribution models can predict the spatial distribution of vector-borne diseases by forming associations between known vector distribution and environmental variables. In response to a changing climate and increasing rates of vector-borne diseases in Europe, model predictions for vector distribution can be used to improve surveillance. However, the field lacks standardisation with little consensus as to what sample size produces reliable models.ObjectiveDetermine the optimum sample size for models developed with the machine learning algorithm, Random Forest, and different sample ratios.Materials and methodsTo overcome limitations with real vector data, a simulated vector with a fully known distribution in 10 test sites across Europe was used to randomly generate different samples sizes. The test sites accounted for varying habitat suitability and the vector’s relative occurrence area. 9,000 Random Forest models were developed with 24 different sample sizes (between 10–5,000) and three sample ratios with varying proportions of presence and absence data (50:50, 20:80, and 40:60, respectively). Model performance was evaluated using five metrics: percentage correctly classified, sensitivity, specificity, Cohen’s Kappa, and Area Under the Curve. The metrics were grouped by sample size and ratio. The optimum sample size was determined when the 25th percentile met thresholds for excellent performance, defined as: 0.605–0.804 for Cohen’s Kappa and 0.795–0.894 for the remaining metrics (to three decimal places).ResultsFor balanced sample ratios, the optimum sample size for reliable models fell within the range of 750–1,000. Estimates increased to 1,100–1,300 for unbalanced samples with a 40:60 ratio of presence and absence data, respectively. Comparatively, unbalanced samples with a 20:80 ratio of presence and absence data did not produce reliable models with any of the sample sizes considered.ConclusionTo our knowledge, this is the first study to use a simulated vector to identify the optimum sample size for Random Forest models at this resolution (≤1 km2) and extent (≥10,000 km2). These results may improve the reliability of model predictions, optimise field sampling, and enhance vector surveillance in response to changing climates. Further research may seek to refine these estimates and confirm transferability to real vectors.

description: The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature class and its derivation is provided within the

Under the direction and funding of the National Cooperative Mapping Program with guidance and encouragement from the United States Geological Survey (USGS), a digital database of three-dimensional (3D) vector data, displayed as two-dimensional (2D) data-extent bounding polygons. This geodatabase is to act as a virtual and digital inventory of 3D structure contour and isopach vector data for the USGS National Geologic Synthesis (NGS) team. This data will be available visually through a USGS web application and can be queried using complimentary nonspatial tables associated with each data harboring polygon. This initial publication contains 60 datasets collected directly from USGS specific publications and federal repositories. Further publications of dataset collections in versioned releases will be annotated in additional appendices, respectfully. These datasets can be identified from their specific version through their nonspatial tables. This digital dataset contains spatial extents of the 2D geologic vector data as polygon features that are attributed with unique identifiers that link the spatial data to nonspatial tables that define the data sources used and describe various aspects of each published model. The nonspatial DataSources table includes full citation and URL address for both published model reports, any digital model data released as a separate publication, and input type of vector data, using several classification schemes. A tabular glossary defines terms used in the dataset. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.