The global workstation desktop market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $25 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching an estimated $40 billion by 2033. This expansion is fueled by several key factors. The rising adoption of advanced technologies like artificial intelligence (AI), machine learning (ML), and big data analytics across industries such as CAD/CAM, Geographic Information Systems (GIS), and simulation necessitates high-performance computing capabilities readily provided by workstation desktops. Furthermore, the increasing need for enhanced visualization and processing power in sectors like plane image processing and media production contributes significantly to market growth. Geographic expansion, particularly in developing economies with growing technological infrastructure and increasing digitalization, further propels the market forward. The demand for specialized workstations tailored to specific application needs, like universal and dedicated workstations, also contributes to market segmentation and expansion. However, certain restraints are impacting market growth. The high initial investment cost of workstation desktops can be prohibitive for some businesses, especially small and medium-sized enterprises (SMEs). Additionally, the rapid advancement of technology necessitates frequent upgrades, leading to higher overall expenditure. Competition from cloud-based solutions and thin clients presents another challenge. Despite these constraints, the continuous innovation in processing power, graphics capabilities, and energy efficiency, alongside increasing demand for high-performance computing, is expected to ensure the sustained growth of the workstation desktop market in the forecast period. The market will likely witness increased competition among key players like Hewlett Packard Enterprise (HPE), Dell, Lenovo, and others, focusing on innovation and catering to niche market segments.

The global workstation desktop market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by the rising adoption of high-performance computing (HPC) in fields like CAD/CAM, GIS, and image processing, where powerful workstations are crucial for complex simulations and data analysis. The shift towards cloud-based solutions and virtualization is also influencing the market, with businesses seeking optimized workflows and reduced IT infrastructure costs. This trend is likely to lead to a surge in demand for high-end, versatile workstations capable of handling both local and cloud-based workloads. Furthermore, the burgeoning gaming industry contributes to the market's growth, as professional gamers and content creators require powerful machines for seamless performance and high-resolution graphics rendering. Competition among major players like Hewlett Packard Enterprise, Dell, and Lenovo is intensifying, leading to innovations in processing power, memory capacity, and graphics capabilities. This competitive landscape fosters continuous improvements in workstation performance and affordability, further driving market expansion. Despite these positive drivers, the market faces some challenges. The high initial cost of workstations can be a barrier for entry, particularly for smaller businesses and individual users. Furthermore, rapid technological advancements necessitate frequent upgrades, adding to the overall cost of ownership. The fluctuating prices of key components, such as GPUs and processors, also contribute to market uncertainty. However, these challenges are being mitigated by the increasing availability of flexible financing options and leasing models, making high-performance computing more accessible. Geographic segmentation reveals a strong concentration of demand in North America and Europe, with the Asia-Pacific region exhibiting significant growth potential due to rapid industrialization and technological advancements. The projected CAGR (let's assume a conservative 7% based on industry trends) suggests a consistent and healthy expansion of the market over the forecast period (2025-2033). The market segmentation by application (CAD/CAM, GIS, etc.) and type (universal vs. dedicated workstations) offers valuable insights into specific market niches and future growth opportunities.

Data source is the Office of Tax and Revenue’s Computer-Assisted Mass Appraisal (CAMA) system. The CAMA system is used by the Assessment Division (AD) within the Real Property Tax Administration to value real estate for ad valorem real property tax purposes.The intent of this data is to provide a sale history for active properties listed among the District of Columbia’s real property tax assessment roll. This data is updated daily. The AD constantly maintains sale data, adding new data and updating existing data. Daily updates represent a “snapshot” at the time the data was extracted from the CAMA system, and data is always subject to change.

Data source is the Office of Tax and Revenue’s Computer-Assisted Mass Appraisal (CAMA) system. The CAMA system is used by the Assessment Division (AD) within the Real Property Tax Administration to value real estate for ad valorem real property tax purposes.The intent of this data is to provide a sale history for active properties listed among the District of Columbia’s real property tax assessment roll. This data is updated daily. The AD constantly maintains sale data, adding new data and updating existing data. Daily updates represent a “snapshot” at the time the data was extracted from the CAMA system, and data is always subject to change.

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to or the know-how to take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the upload, storage, and sharing of a broad class of hydrologic data including time series, geographic features and raster datasets, multidimensional space-time data, and other structured collections of data. Web service tools and a Python client library provide researchers with access to HPC resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This presentation will illustrate the use of web based data and computation services from both the browser and desktop client software. These web-based services implement the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation, generation of hydrology-based terrain information, and preparation of hydrologic model inputs. They allow users to develop scripts on their desktop computer that call analytical functions that are executed completely in the cloud, on HPC resources using input datasets stored in the cloud, without installing specialized software, learning how to use HPC, or transferring large datasets back to the user's desktop. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Slides for AGU 2015 presentation IN51C-03, December 18, 2015

Geo-referenced datasets.

Shapefile used in the various maps in the study. Visit https://dataone.org/datasets/sha256%3A2fdaa83821076dc77d906d53f13fd8aaa6ecb2f8bf1e16082352037b5459f465 for complete metadata about this dataset.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We used ERDAS Imagine ® Professional 9.2, ENVI ® 4.5, and ArcGIS ® 9.3 with Arc Workstation to develop the vegetation spatial database. Existing GIS datasets that we used to provide mapping information include a NPS park boundary shapefile for VICK (including a 100 meter buffer boundary around the Louisiana Circle, South Fort, and Navy Circle satellite units), a land cover shapefile created by the NWRC (Rangoonwala et al. 2007), and the National Elevation Dataset (NED) (used as the source of the 10-meter elevation model and derived streams, slope, and hillshade). To make the entire spatial data set consistent with NPSVI policies to map only to park boundaries, we clipped the vegetation in and around the previously buffered areas around the Louisiana Circle, South Fort, and Navy Circle satellite unit NPS boundaries. We also added to the spatial database vegetation polygons for the previously omitted Grant’s Canal satellite unit by heads-up digitizing this area from a National Agricultural Information Program (NAIP) image.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Peoria County's main web map application known as the Front Desk. Users can search for property tax and assessment information, tax districts, zoning, utilities, election districts, boundaries aerials, basemaps and much more.

Local libraries and community organizations that will allow you to use computers free of charge. Some locations also offer the following resources: Wi-fi, Printing, Internet, iPad rental, Classes, and special areas for kids and teens.

CAMA_2004_HARF File Geodatabase Feature Class Thumbnail Not Available Tags Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, CAMA, Database, Structure, Appraisal Summary Detailed structural information for parcels. Description The CAMA (Computer Assisted Mass Appraisal) Database is created on a yearly basis using data obtained from the State Department of Assessments and Taxation (SDAT). Each yearly download contains additional residential housing characteristics as available for parcels included in the CAMA Database and the CAMA supplementary databases for each jurisdiction.. Documentation for CAMA, including thorough definitions for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the CAMA Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the CAMA Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the CAMA Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the CAMA Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 194 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." Credits Maryland Department of Planning Use limitations BES use only. Extent West -76.568860 East -76.081594 North 39.726323 South 39.392952 Scale Range There is no scale range for this item.

The geospatial data fusion market is experiencing robust growth, driven by increasing demand for location-based intelligence across diverse sectors. The convergence of various data sources, including satellite imagery, sensor data, and geographic information systems (GIS), is fueling the adoption of advanced geospatial analytics. This market is segmented by delivery model (SaaS, PaaS) and application (earth observation, computer vision, military & security, and others). The SaaS model currently holds a significant market share due to its scalability and accessibility, while the demand for earth observation and computer vision applications is rapidly expanding, propelled by advancements in AI and machine learning. Government initiatives focused on national security and infrastructure development are further boosting market growth. North America and Europe currently dominate the market, but the Asia-Pacific region is projected to witness the fastest growth in the coming years due to rising investments in infrastructure and technological advancements. Competitive dynamics are characterized by a mix of established GIS vendors and specialized geospatial data fusion companies. Future growth will be influenced by factors such as increased data volumes, technological advancements in data processing and analytics, and ongoing investments in research and development. While precise figures are not provided, assuming a moderate CAGR (let's estimate at 15% for illustrative purposes), and a 2025 market size of $5 billion (a reasonable estimate considering the mentioned companies and applications), the market is poised for significant expansion. The restraints on market growth are likely associated with high initial investment costs for implementation, the need for skilled professionals to interpret the fused data, and concerns regarding data security and privacy. However, these challenges are gradually being addressed through the development of user-friendly software and robust data security protocols. The market's trajectory suggests a continuous upward trend, with growth significantly influenced by the adoption of innovative geospatial technologies and increased government and private sector investment.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Funded by a grant from the Sloan Foundation, and with support from Massachusetts Open Cloud, the Center for Geographic Analysis(CGA) at Harvard developed a “big geodata”, remotely hosted, real-time-updated dataset which is a prototype for a new data type hosted outside Dataverse which supports streaming updates, and is accessed via an API. The CGA developed 1) the software and hardware platform to support interactive exploration of a billion spatio-temporal objects, nicknamed the "BOP" (billion object platform) 2) an API to provide query access to the archive from Dataverse 3) client-side tools for querying/visualizing the contents of the archive and extracting data subsets. This project is currently no longer active. For more information please see: http://gis.harvard.edu/services/project-consultation/project-resume/billion-object-platform-bop. “Geotweets” are tweets containing a GPS coordinate from the originating device. Currently 1-2% of tweets are geotweets, about 8 million per day. The CGA has been harvesting geotweets since 2012.

description: This tool provides a no-cost downloadable software tool that allows users to interact with professional quality GIS maps. Users access pre-compiled projects through a free software product called ArcReader, and are able to open and explore HUD-specific project data as well as design and print custom maps. No special software/map skills beyond basic computer skills are required, meaning users can quickly get started working with maps of their communities.; abstract: This tool provides a no-cost downloadable software tool that allows users to interact with professional quality GIS maps. Users access pre-compiled projects through a free software product called ArcReader, and are able to open and explore HUD-specific project data as well as design and print custom maps. No special software/map skills beyond basic computer skills are required, meaning users can quickly get started working with maps of their communities.

CAMA_2004_BACO File Geodatabase Feature Class Thumbnail Not Available Tags Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, CAMA, Database, Structure, Appraisal Summary Detailed structural information for parcels. Description The CAMA (Computer Assisted Mass Appraisal) Database is created on a yearly basis using data obtained from the State Department of Assessments and Taxation (SDAT). Each yearly download contains additional residential housing characteristics as available for parcels included in the CAMA Database and the CAMA supplementary databases for each jurisdiction.. Documentation for CAMA, including thorough definitions for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the CAMA Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the CAMA Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields). A cursory review of the 2004 version of the CAMA Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the CAMA Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 3999 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords." Credits Maryland Department of Planning Use limitations BES use only. Extent West -76.897802 East -76.335219 North 39.726520 South 39.192836 Scale Range There is no scale range for this item.

Flow accumulation grid generated from 10 meter DEM, Andrews Experimental Forest. This grid is useful for determining the area of land that drains to a point. The user selects a point on the grid, and the value of that point represents the area (in 100 square meters) that drain to the point. This grid can also be used for generating watershed boundaries and stream networks.

Building shape data and codes that support the findings of our paper entitled "Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps" (https://doi.org/10.1080/13658816.2020.1768260)

CAMA_2003_BACI_1

   File Geodatabase Feature Class


   Thumbnail Not Available

   Tags

   There are no tags for this item.




   Summary

   There is no summary for this item.


   Description


   MD Property View 2003 CAMA Database. For more information on the CAMA Database refer to the enclosed documentation. This layer was edited to remove spatial outliers in the CAMA Database. Spatial outliers are those points that were not geocoded and as a result fell outside of the Baltimore City Boundary. 254 spatial outliers were removed from this layer.


   Credits

   There are no credits for this item.


   Use limitations

   There are no access and use limitations for this item.


   Extent



   West -76.713415  East -76.526101 

   North 39.374324  South 39.200707 




   Scale Range

   There is no scale range for this item.