The Computer Vision in Geospatial Imagery market is experiencing robust growth, driven by increasing demand for accurate and efficient geospatial data analysis across various sectors. Advancements in artificial intelligence (AI), deep learning, and high-resolution imaging technologies are fueling this expansion. The market's ability to extract valuable insights from aerial and satellite imagery is transforming industries such as agriculture, urban planning, environmental monitoring, and defense. Applications range from precision agriculture using drone imagery for crop health monitoring to autonomous vehicle navigation and infrastructure inspection using high-resolution satellite data. The integration of computer vision with cloud computing platforms facilitates large-scale data processing and analysis, further accelerating market growth. We estimate the 2025 market size to be approximately $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is expected to continue, driven by increasing adoption of advanced analytics and the need for real-time geospatial intelligence. Several factors contribute to this positive outlook. The decreasing cost of high-resolution sensors and cloud computing resources is making computer vision solutions more accessible. Furthermore, the growing availability of large datasets for training sophisticated AI models is enhancing the accuracy and performance of computer vision algorithms in analyzing geospatial data. However, challenges remain, including data privacy concerns, the need for robust data security measures, and the complexity of integrating diverse data sources. Nevertheless, the overall market trend remains strongly upward, with significant opportunities for technology providers and users alike. The key players listed—Alteryx, Google, Keyence, and others—are actively shaping this landscape through innovative product development and strategic partnerships.

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.

The field computer market is booming, projected to reach $6.07 billion by 2033 with a 5.9% CAGR. Discover key drivers, trends, and restraints shaping this dynamic industry, including insights from leading vendors like Panasonic and Getac. Explore regional market share and growth projections in this comprehensive analysis.

Updated for 2013-17: US Census American Community Survey data table for: COMPUTER AND INTERNET USAGE subject area. Provides information about: TYPES OF COMPUTERS IN HOUSEHOLD for the universe of: HOUSEHOLDS. These data are extrapolated estimates only, based on sampling; they are not actual complete counts. The data is based on 2010 Census Tracts. Table ACS_B28001_TYPECOMPUTERHSHLD contains both the Estimate value in the E item for the census topic and an adjacent M item which defines the Margin of Error for the value. The Margin of Error (MOE) is the plus/minus range for the item estimate value, where the range between the Estimate minus the Margin of Error and the Estimate plus the Margin of Error defines the 90% confidence interval of the item value. Many of the Margin of Error values are significant relative to the size of the Estimate value. This table contains 11 item(s) extracted from a larger sequence table. This extracted subset represents that portion of the sequence that is considered high priority. Other portions of this sequence that are not included can be identified in the data dictionary information provided in the Supplemental Information section. This table information is also provided as a customized layer file: B28001_AREA_TYPECOMPUTERHSHLD.lyr where the table information is joined to the 2010 TRACTS_AREA census geography on the GEOID item. Both the table and customized lyr file name do not contain the year descriptor (i.e. 2013-2017) for the current ACS series. This is intentional in order to maintain the same table name in each successive ACS update. The alias of each item's (E)stimate and (M)easure of Error value stores this year date information as beginning YY and ending YY, i.e., 'E1317' and 'M1317' followed by the rest of the alias description. In this way users of the data tables or lyr files that support field aliases can determine which ACS series is being represented by the current table contents. The next 5-year sample of ACS, representing the current year minus 1, becomes available in December of each year. For example, the next series - 2014 through 2018 - will become available at the end of 2019. The new 2017 data will be posted to the Spatial Data Warehouse by January 2019. The previous series of data is retired to the Historical Data Library geodatabase (according to the ACS series end date) from where it can be accessed if needed.

This paper underlines the task characteristics of the emergency management life cycle. Moreover, the characteristics of three ubiquitous technologies including RFID, handheld computers and GIS are discussed and further used as a criterion to evaluate their potential for emergency management tasks. Built on a rather loose interpretation of Task-technology Fit model, a conceptual model presented in this paper advocates that a technology that offers better features for task characteristics is more likely to be adopted in emergency management. Empirical findings presented in this paper reveal the significance of task characteristics and their role in evaluating the suitability of three ubiquitous technologies before their actual adoption in emergency management.

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 storage and sharing of a broad class of hydrologic data including time series, geographic features and rasters, multidimensional space-time data and structured collections of data representing river geometry. Web service tools and a python client library provide researchers with access to high performance computing 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 talk will illustrate web and client based use of data services that support the delineation of watersheds to define a modeling domain, then extract terrain and land use information to automatically configure the inputs required for hydrologic models. These services support the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation and generation of hydrology-based terrain information such as wetness index and stream networks. These services also support the derivation of inputs for the Utah Energy Balance snowmelt model used to address questions such as how climate, land cover and land use change may affect snowmelt inputs to runoff generation. 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.

Presentation at Kansas University GIS Days November 18, 2015

This dataset was created to be used in my Capstone Project for the Google Data Analytics Professional Certificate. Data was web scraped from the state websites to combine the GIS information like FIPS, latitude, longitude, and County Codes by both number and Mailing Number.

RStudio was used for this web scrape and join. For details on how it was done you can go to the following link for my Github repository.

Feel free to follow my Github or LinkedIn profile to see what I end up doing with this Dataset.

A parcel-based land use summary of the Denver region All files are uploaded in a zip archive file format- they will need to be extracted before opening in ArcGIS, QGIS, or similar programs. In general we recommend using the gdb source rather than shp sources for data due to some data loss and field truncation in the shape file archives. Projected Coordinate System NAD 1983 HARN StatePlane Colorado Central FIPS 0502 (US Feet) WKID: 2877 Sources Colorado Public Parcels – link Aggregated by the Governor's Office of Information Technology Geospatial Information Systems; contains all public parcels in the state, including land use codes supplied by counties and other jurisdictions. Sourced on 7/9/2025. Zoning 2023 – link Zoning shapefile sourced from DRCOG, used to supply possible ‘land use’ where land use codes were not provided in the Colorado Public Parcels file. Land Use Categorizations Below are the land use codes I categorized parcels by Residential: Any residence land use (single family, multi-unit, senior) Zoning Follow Up: Should not be in the final dataset – used to indicate there's no land use data and needs to be backfilled with zoning classifications Vacant: Parcel indicated as vacant Commercial: Any commercial use, including office, retail Exempt/Government: Exempt land use or government land use (eg: city hall, fire station) Agricultural: Agricultural, or ranch use Other/Unknown: Some other use / cannot be determined Industrial: Industrial, including meat packing School: Schools (K‑12, college, public and private) Mixed Use: A parcel specifically marked as mixed use Open Space/Parks/Recreation: Open space, park, outdoor recreation (eg: cabins, camping, etc) Medical: Hospitals, medical offices, etc Caveats Many parcels did not contain land use codes or contained land use codes that could not be discerned. In that case, zoning designations were appended to estimate the land use. Even with the above process, many parcels were missing a land use classification. Files & Feature Layers Land Use Parcel Standardization.xlsx: A spreadsheet where I standardized land use codes into the categories above. The tab Land Use Codes is where categorizations were based on the parcels’ land use codes and descriptions, while Zoning Code Follow Up used the zoning classification that had greatest geographic overlap with the parcel. public_parcel_drcog.shp: The original Colorado Public Parcels file, with the majority‑overlapping zoning code from Zoning 2023 added, and the land use categorizations from Land Use Parcel Standardization appended. parcel_land_use.shp: A final feature class, derived from public_parcel_drcog and dissolved by the zoning categorization appended from Land Use Parcel Standardization. Land Use Data.gdb: Contains all the above feature classes.

This layer shows Technology Access by Household. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer represents the underlying data for several data visualizations on the Tempe Equity Map.Data visualized as a percent of total households in given census tract.Layer includes:Key demographicsTotal Households % With a Desktop or Laptop Computer% With only a Desktop or Laptop% With a Smartphone% With only a Smartphone% With a Tablet% With only a tablet% With other type of computing device% With other type of computing device only% No computerCurrent Vintage: 2017-2021ACS Table(s): S2801 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: Dec 8, 2022Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

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.

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

Fish habitat model and visualization of habitat niche using GRASS GIS r.fuzzy.system and R

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.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

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.

Purpose: This is an ArcGIS Pro template that GIS Specialists can use to identify vulnerable populations and special needs infrastructure most at risk to flooding events.How does it work?Determine and understand the Place Vulnerability (based on Cutter et al. 1997) and the Special Needs Infrastructure for an area of interest based on Special Flood Hazard Zones, Social Vulnerability Index, and the distribution of its Population and Housing units. The final product will be charts of the data distribution and a Hosted Feature Layer. See this Story Map example for a more detailed explanation.This uses the FEMA National Flood Hazard Layer as an input (although you can substitute your own flood hazard data), check availability for your County before beginning the Task: FEMA NFHL ViewerThe solution consists of several tasks that allow you to:Select an area of interest for your Place Vulnerability Analysis. Select a Hazard that may occur within your area of interest.Select the Social Vulnerability Index (SVI) features contained within your area of interest using the CDC’s Social Vulnerability Index (SVI) – 2016 overall SVI layer at the census tract level in the map.Determine and understand the Social Vulnerability Index for the hazard zones identified within you area of interest.Identify the Special Needs Infrastructure features located within the hazard zones identified within you area of interest.Share your data to ArcGIS Online as a Hosted Feature Layer.FIRST STEPS:Create a folder C:\GIS\ if you do not already have this folder created. (This is a suggested step as the ArcGIS Pro Tasks does not appear to keep relative paths)Download the ZIP file.Extract the ZIP file and save it to the C:\GIS\ location on your computer. Open the PlaceVulnerabilityAnalysis.aprx file.Once the Project file (.aprx) opens, we suggest the following setup to easily view the Tasks instructions, the Map and its Contents, and the Databases (.gdb) from the Catalog pane.The following public web map is included as a Template in the ArcGIS Pro solution file: Place Vulnerability Template Web MapNote 1:As this is a beta version, please take note of some pain points:Data input and output locations may need to be manually populated from the related workspaces (.gdb) or the tools may fail to run. Make sure to unzip/extract the file to the C:\GIS\ location on your computer to avoid issues.Switching from one step to the next may not be totally seamless yet.If you are experiencing any issues with the Flood Hazard Zones service provided, or if the data is not available for your area of interest, you can also download your Flood Hazard Zones data from the FEMA Flood Map Service Center. In the search, use the FEMA ID. Once downloaded, save the data in your project folder and use it as an input.Note 2:In this task, the default hazard being used are the National Flood Hazard Zones. If you would like to use a different hazard, you will need to add the new hazard layer to the map and update all query expressions accordingly.For questions, bug reports, or new requirements contact pdoherty@publicsafetygis.org

This dataset consists of: a colour digital orthophoto of Taylor Rookery; and vector data resulting from 1:5000 scale topographic mapping of Taylor Rookery. The vector data are formatted according to the SCAR Feature Catalogue (see link below).

This layer shows Computers and Internet Use. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show Percentage of Households with a Broadband Internet Subscription. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): DP02, S2801Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2022National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

The 4 global maps of pyroxene-related spectral parameters derived from data coming from the VIR mapping spectrometer onboard NASA/Dawn acqusition campaing at Vesta.

CAMA_2004_CARR

   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 399 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 -77.306843  East -76.779379 

   North 39.727017  South 39.346946 




   Scale Range

   There is no scale range for this item.