Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of res ...

The global GIS data collector market is experiencing robust growth, driven by increasing adoption of precision agriculture, expanding infrastructure development projects, and the rising demand for accurate geospatial data across various industries. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $4.2 billion by 2033. Key drivers include the increasing availability of affordable and high-precision GPS technology, coupled with advancements in data processing and cloud-based solutions. The integration of GIS data collectors with other technologies, such as drones and IoT sensors, is further fueling market expansion. The demand for high-precision GIS data collectors is particularly strong in sectors like surveying, mapping, and construction, where accuracy is paramount. While the market faces challenges such as high initial investment costs and the need for specialized expertise, the overall growth trajectory remains positive. The market is segmented by application (agriculture, industrial, forestry, and others) and by type (general precision and high precision). North America and Europe currently hold significant market shares, but the Asia-Pacific region is anticipated to experience rapid growth in the coming years due to substantial infrastructure development and increasing government investments in geospatial technologies. The competitive landscape is characterized by both established players like Trimble, Garmin, and Hexagon (Leica Geosystems) and emerging companies offering innovative solutions. These companies are constantly innovating, integrating advanced technologies like AI and machine learning to enhance data collection and analysis capabilities. This competition is driving down prices and improving product quality, benefiting end-users. The increasing use of mobile GIS and cloud-based data management solutions is also transforming the industry, making data collection and analysis more accessible and efficient. Future growth will be largely influenced by the advancement of 5G networks, enabling faster data transmission and real-time applications, and the increasing adoption of automation and AI in data processing workflows. Furthermore, government regulations promoting the use of accurate geospatial data for sustainable development and environmental monitoring are creating new opportunities for the market’s expansion.

GIS Data Collector Market Outlook

The global GIS Data Collector market size is anticipated to grow from USD 4.5 billion in 2023 to approximately USD 12.3 billion by 2032, at a compound annual growth rate (CAGR) of 11.6%. The growth of this market is largely driven by the increasing adoption of GIS technology across various industries, advances in technology, and the need for effective spatial data management.

An important factor contributing to the growth of the GIS Data Collector market is the rising demand for geospatial information across different sectors such as agriculture, construction, and transportation. The integration of advanced technologies like IoT and AI with GIS systems enables the collection and analysis of real-time data, which is crucial for effective decision-making. The increasing awareness about the benefits of GIS technology and the growing need for efficient land management are also fuelling market growth.

The government sector plays a significant role in the expansion of the GIS Data Collector market. Governments worldwide are investing heavily in GIS technology for urban planning, disaster management, and environmental monitoring. These investments are driven by the need for accurate and timely spatial data to address critical issues such as climate change, urbanization, and resource management. Moreover, regulatory policies mandating the use of GIS technology for infrastructure development and environmental conservation are further propelling market growth.

Another major growth factor in the GIS Data Collector market is the continuous technological advancements in GIS software and hardware. The development of user-friendly and cost-effective GIS solutions has made it easier for organizations to adopt and integrate GIS technology into their operations. Additionally, the proliferation of mobile GIS applications has enabled field data collection in remote areas, thus expanding the scope of GIS technology. The advent of cloud computing has further revolutionized the GIS market by offering scalable and flexible solutions for spatial data management.

Regionally, North America holds the largest share of the GIS Data Collector market, driven by the presence of key market players, advanced technological infrastructure, and high adoption rates of GIS technology across various industries. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, primarily due to rapid urbanization, government initiatives promoting GIS adoption, and increasing investments in smart city projects. Other regions such as Europe, Latin America, and the Middle East & Africa are also experiencing significant growth in the GIS Data Collector market, thanks to increasing awareness and adoption of GIS technology.

The role of a GPS Field Controller is becoming increasingly pivotal in the GIS Data Collector market. These devices are essential for ensuring that data collected in the field is accurate and reliable. By providing real-time positioning data, GPS Field Controllers enable precise mapping and spatial analysis, which are critical for applications such as urban planning, agriculture, and transportation. The integration of GPS technology with GIS systems allows for seamless data synchronization and enhances the efficiency of data collection processes. As the demand for real-time spatial data continues to grow, the importance of GPS Field Controllers in the GIS ecosystem is expected to rise, driving further innovations and advancements in this segment.

Component Analysis

The GIS Data Collector market is segmented by component into hardware, software, and services. Each of these components plays a crucial role in the overall functionality and effectiveness of GIS systems. The hardware segment includes devices such as GPS units, laser rangefinders, and mobile GIS devices used for field data collection. The software segment encompasses various GIS applications and platforms used for data analysis, mapping, and visualization. The services segment includes consulting, training, maintenance, and support services provided by GIS vendors and solution providers.

In the hardware segment, the demand for advanced GPS units and mobile GIS devices is increasing, driven by the need for accurate and real-time spatial data collection. These devices are equipped with high-precision sensors and advanced features such as real-time kinematic (RTK) positioning, which enhance

This data set contains land surface elevations on dry and wadeable portions of transects for pre-construction hydrographic surveys on the Missouri River below Gavins Point Dam for the Emergent Sandbar Habitat construction project near River Mile 761.4. Data tie- ins to local benchmarks also are included

GPS horizontal and vertical position data were collected on the Nisqually River, McAllister Creek and Nisqually River Delta to survey in water surface, instrumentation and delta structures for to reference North American Vertical Datum 1988 (NAVD88). These data are housed in .csv file named “Nisqually GPS Data” and are sorted by date and time. The position data are grouped by data collection methods Point and Topo. Point method collected position data for 180 seconds and was used to survey surface water and instrumentation elevation. Topo method collected position point data for 1 second and was used for surveying delta bathymetry elevation. Data were collected using the available RTN-GPS network provided by the Washington State Reference Network and using a Trimble R8 GPS antenna mounted on a 2-meter rod. Position data are labeled with descriptors such as “WS” (water surface) or “Delta” which refer to the feature surveyed. Check-in/check-out procedures were satisfied using reference marker Station: pid_sy0708. Two check-in orthometric heights were collected (60.05 and 60.10 m) and following point and topo data collection one check-out orthometric height (60.04 m) was collected. Bathymetric data (Topo method) was collected across the Nisqually River Delta starting at the left bank of McAllister Creek (MC2) and ended on the right bank of tidal channel D4. A total of 2,505 positions were surveyed using the topo method and positions were labeled as “delta-trav###”. Delta elevation ranged from 3.44 to -1.64 meters (NAVD88). Rod and antenna were held at a fixed level marked on both upper rod and technician for maintaining a constant 2 meter height above the walking surface. The bottom half of the rod was removed during topo data collection for ease of walking to avoid rod tip drag and keeping an even pace along the delta structures. Tidal channel bathymetry data consists of transects between banks with position names containing the tidal channel name and distance upstream or downstream of deployed sensor. Only D4 and D3 tidal channel bathymetric data sets were collected. Both D3 (Station ID: “les”) and D4 (Station ID: “are3”) had four tidal channel bathymetry transects collected which consisting of a 10 and 20 meter upstream and downstream of deployed sensor transects. Point data were collected at sites with sensors collecting water depth (WL) time-series data. GPS data was collected by holding the rod/antenna unit at a bubble-level static positioned for 3 minutes (180 epochs) during data collection. Point data were water surface elevations which were used to provide offsets for converting recorded water level (WL) data by sensors to referenced NAVD88.

NAV

This data set contains land surface elevations on dry and wadeable portions of transects for the hydrographic surveys on the Missouri River below Gavins Point Dam near River Mile 769.8. This data provides land surface elevations of shallow-water, shore, and highbank for the Missouri River following construction of Emergent Sandbar Habitat.

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our Geospatial insights are sourced through partnerships with tier-1 app developers and a unique data collection method. The low-latency delivery ensures real-time insights, setting us apart and providing unparalleled benefits and use cases for Location Data, Mobile Location Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is unwavering. All data is collected with clear privacy notices, and our opt-in/out management ensures transparent control over data collection, use, and distribution.

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Number of days with GPS data per participant, by different time thresholds (n = 75).

As of March 2021, Waze was the mobile GPN navigation app found to collect the largest amount of data from global iOS users, with 21 data points collected across all examined segments. Maps.me collected a total of 20 data points from its users, including five data points on contact information. Hiking and trail GPS map Gaia followed, with 13 data points, respectively.

Three Global Positioning System (GPS) datasets collected during the MPD-NetDemo field campaign. The data were collected between 16 April 2019 and 22 July 2019. The GFZ German Research Centre for Geosciences in Potsdam, Germany processed the data to provide precipitable water vapor (PWV) fields.

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our Mobile Location Data insights are sourced through partnerships with tier-1 app developers and a unique data collection method. The low-latency delivery ensures real-time insights, setting us apart and providing unparalleled benefits and use cases for Location Data, Places Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is unwavering. Clear and compliant privacy notices accompany our data collection process. Opt-in/out management empowers users over data distribution.

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This data set contains GPS data collected by the FDEP Bureau of Surveying and Mapping (BSM). Data was first imported from the NGS Online Positional User Service (OPUS). On 1/10/2017 the import was performed based on all OPUS submittals performed by Rudolphe Konou of FDEP BSM. The file was imported to a geodatabase. Some of the original fields were dropped from the data set and some maintenance of the other fields was performed to match BSM needs for a web application.Since inception, the file is updated via a Geoform as solutions are returned to FDEP from OPUS. Point of Contact:Bryan Shoaf, FDEP Division of State Lands850-245-2619bryan.shoaf@dep.state.fl.us

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our location data insights are sourced through partnerships with tier-1 app developers. The raw GPS data, delivered at an hourly cadence, provides unparalleled benefits and use cases for Transport and Logistic Data, Mobile Location Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is unwavering. All data is collected with clear privacy notices, and our opt-in/out management ensures transparent control over data collection, use, and distribution.

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This data set contains land surface elevations on dry and wadeable portions of transects for the hydrographic surveys on the Missouri River below Gavins Point Dam near River Mile 761.4. This data provides land surface elevations of shallow-water, shore, and highbank for the Missouri River following construction of Emergent Sandbar Habitat.

Comparison of Pre- and Post-GPS Feasibility and Acceptability Survey Items.

The construction of this data model was adapted from the Telvent Miner & Miner ArcFM MultiSpeak data model to provide interface functionality with Milsoft Utility Solutions WindMil engineering analysis program. Database adaptations, GPS data collection, and all subsequent GIS processes were performed by Southern Geospatial Services for the Town of Apex Electric Utilities Division in accordance to the agreement set forth in the document "Town of Apex Electric Utilities GIS/GPS Project Proposal" dated March 10, 2008. Southern Geospatial Services disclaims all warranties with respect to data contained herein. Questions regarding data quality and accuracy should be directed to persons knowledgeable with the forementioned agreement.The data in this GIS with creation dates between March of 2008 and April of 2024 were generated by Southern Geospatial Services, PLLC (SGS). The original inventory was performed under the above detailed agreement with the Town of Apex (TOA). Following the original inventory, SGS performed maintenance projects to incorporate infrastructure expansion and modification into the GIS via annual service agreements with TOA. These maintenances continued through April of 2024.At the request of TOA, TOA initiated in house maintenance of the GIS following delivery of the final SGS maintenance project in April of 2024. GIS data created or modified after April of 2024 are not the product of SGS.With respect to SGS generated GIS data that are point features:GPS data collected after January 1, 2013 were surveyed using mapping grade or survey grade GPS equipment with real time differential correction undertaken via the NC Geodetic Surveys Real Time Network (VRS). GPS data collected prior to January 1, 2013 were surveyed using mapping grade GPS equipment without the use of VRS, with differential correction performed via post processing.With respect to SGS generated GIS data that are line features:Line data in the GIS for overhead conductors were digitized as straight lines between surveyed poles. Line data in the GIS for underground conductors were digitized between surveyed at grade electric utility equipment. The configurations and positions of the underground conductors are based on TOA provided plans. The underground conductors are diagrammatic and cannot be relied upon for the determination of the actual physical locations of underground conductors in the field.The Service Locations feature class was created by Southern Geospatial Services (SGS) from a shapefile of customer service locations generated by dataVoice International (DV) as part of their agreement with the Town of Apex (TOA) regarding the development and implemention of an Outage Management System (OMS).Point features in this feature class represent service locations (consumers of TOA electric services) by uniquely identifying the features with the same unique identifier as generated for a given service location in the TOA Customer Information System (CIS). This is also the mechanism by which the features are tied to the OMS. Features are physically located in the GIS based on CIS address in comparison to address information found in Wake County GIS property data (parcel data). Features are tied to the GIS electric connectivity model by identifying the parent feature (Upline Element) as the transformer that feeds a given service location.SGS was provided a shapefile of 17992 features from DV. Error potentially exists in this DV generated data for the service location features in terms of their assigned physical location, phase, and parent element.Regarding the physical location of the features, SGS had no part in physically locating the 17992 features as provided by DV and cannot ascertain the accuracy of the locations of the features without undertaking an analysis designed to verify or correct for error if it exists. SGS constructed the feature class and loaded the shapefile objects into the feature class and thus the features exist in the DV derived location. SGS understands that DV situated the features based on the address as found in the CIS. No features were verified as to the accuracy of their physical location when the data were originally loaded. It is the assumption of SGS that the locations of the vast majority of the service location features as provided by DV are in fact correct.SGS understands that as a general rule that DV situated residential features (individually or grouped) in the center of a parcel. SGS understands that for areas where multiple features may exist in a given parcel (such as commercial properties and mobile home parks) that DV situated features as either grouped in the center of the parcel or situated over buildings, structures, or other features identifiable in air photos. It appears that some features are also grouped in roads or other non addressed locations, likely near areas where they should physically be located, but that these features were not located in a final manner and are either grouped or strung out in a row in the general area of where DV may have expected they should exist.Regarding the parent and phase of the features, the potential for error is due to the "first order approximation" protocol employed by DV for assigning the attributes. With the features located as detailed above, SGS understands that DV identified the transformer closest to the service location (straight line distance) as its parent. Phase was assigned to the service location feature based on the phase of the parent transformer. SGS expects that this protocol correctly assigned parent (and phase) to a significant portion of the features, however this protocol will also obviously incorretly assign parent in many instances.To accurately identify parent for all 17992 service locations would require a significant GIS and field based project. SGS is willing to undertake a project of this magnitude at the discretion of TOA. In the meantime, SGS is maintaining (editing and adding to) this feature class as part of the ongoing GIS maintenance agreement that is in place between TOA and SGS. In lieu of a project designed to quality assess and correct for the data provided by DV, SGS will verify the locations of the features at the request of TOA via comparison of the unique identifier for a service location to the CIS address and Wake County parcel data address as issues arise with the OMS if SGS is directed to focus on select areas for verification by TOA. Additionally, as SGS adds features to this feature class, if error related to the phase and parent of an adjacent feature is uncovered during a maintenance, it will be corrected for as part of that maintenance.With respect to the additon of features moving forward, TOA will provide SGS with an export of CIS records for each SGS maintenance, SGS will tie new accounts to a physical location based on address, SGS will create a feature for the CIS account record in this feature class at the center of a parcel for a residential address or at the center of a parcel or over the correct (or approximately correct) location as determined via air photos or via TOA plans for commercial or other relevant areas, SGS will identify the parent of the service location as the actual transformer that feeds the service location, and SGS will identify the phase of the service address as the phase of it's parent.Service locations with an ObjectID of 1 through 17992 were originally physically located and attributed by DV.Service locations with an ObjectID of 17993 or higher were originally physically located and attributed by SGS.DV originated data are provided the Creation User attribute of DV, however if SGS has edited or verified any aspect of the feature, this attribute will be changed to SGS and a comment related to the edits will be provided in the SGS Edits Comments data field. SGS originated features will be provided the Creation User attribute of SGS. Reference the SGS Edits Comments attribute field Metadata for further information.

This is a 20-year long database of GPS data collected by geodetic surveys carried out over the seismically and volcanically active eastern Sicily, for a total of more than 6300 measurements. Data have been convertedi nto the international ASCII compressed RINEX standard in order to be imported and processed by any GPS analysis software. Database is provided with an explorer software for navigating into the dataset by spatial (GIS) and temporal queries.

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GPS Market Size 2024-2028

The GPS market size is forecast to increase by USD 111.6 million, at a CAGR of 22.1% between 2023 and 2028.

The Global Positioning System (GPS) market is experiencing significant growth, driven by increasing investment in satellite deployment and the rising demand for advanced GPS devices. These trends reflect the market's potential for innovation and expansion. However, connectivity issues with GPS pose a notable challenge. As satellite coverage can be disrupted by various factors, ensuring uninterrupted GPS service remains a critical concern. Companies must invest in robust technologies to mitigate these disruptions and maintain reliable connectivity. To capitalize on market opportunities and navigate challenges effectively, businesses should focus on developing advanced GPS solutions that address connectivity concerns while offering enhanced features and functionality.
By doing so, they can cater to the evolving needs of consumers and industries, positioning themselves as leaders in the dynamic the market. Despite this,the market is expected to continue its expansion, driven by technological advancements and growing applications across various industries, including automotive technologies.

What will be the Size of the GPS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The Global Positioning System (GPS) market continues to evolve, with dynamic applications across various sectors. Weather alerts integrated with GPS navigation systems provide real-time information, enhancing safety and convenience for travelers. Head-up displays merge GPS navigation with vehicle data, projecting essential information onto the windshield for easy viewing. Aviation navigation relies on GPS for precise flight tracking and route planning, while autonomous vehicles leverage GPS for positioning and navigation. Automotive navigation systems offer turn-by-turn directions, real-time traffic updates, and subscription models. GNSS receivers provide positioning accuracy for asset tracking in industries like logistics and construction. Smart cities utilize GPS for efficient traffic management, emergency response, and field data collection.

Outdoor navigation systems cater to hikers and adventurers, while security protocols ensure location tracking and positioning accuracy for personal safety. Mapping technologies and navigation services are essential for marine navigation, precision agriculture, and geospatial data collection. Navigation software upgrades, antenna design improvements, and signal strength enhancements continue to drive market innovation. Positioning algorithms and lane guidance systems offer more accurate and efficient navigation solutions. Voice guidance and subscription models cater to diverse user preferences. Road closures and speed limit warnings help optimize travel routes, while satellite positioning and cloud-based services enable remote sensing and real-time data processing.

The ongoing development of GPS technologies and their integration into various industries ensure a continuously evolving market landscape.

How is this GPS Industry segmented?

The GPS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Logistics and transportation
  Construction and mining
  Others


Type

  Handheld GPS Devices
  Vehicle GPS Devices
  Personal GPS Devices
  Asset Tracking Devices
  Smartphone GPS


End-use Industry

  Automotive
  Transportation & Logistics
  Consumer Electronics
  Aerospace & Defense
  Agriculture
  Mining
  Construction
  Healthcare
  Retail & E-commerce


Technology

  GNSS
  A-GPS
  DR-GPS


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Application Insights

The logistics and transportation segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth as businesses increasingly prioritize the optimization of their supply chains. Weather alerts and real-time traffic updates ensure the timely delivery of perishable goods, such as food, maintaining their market value. In the e-commerce sector, GPS navigation systems and voice guidance facilitate on-time delivery, enhancing customer satisfaction. For industries dealing with valuable assets, such as jewelry or electronics, security protocols and location tracking through GPS technology safeguard against the

Small Uncrewed Aircraft Systems (sUAS) were used to collect aerial remote sensing data over Marsh Island, a salt marsh restoration site along New Bedford Harbor, Massachusetts. Remediation of the site will involve direct hydrological and geochemical monitoring of the system alongside the UAS remote sensing data. On July 2nd, 2024, USGS personnel and interns collected natural (RGB) color and infrared (thermal) images and ground control points. These data were processed to produce a high resolution orthomosaics and a digital surface model. Data collection is related to USGS Field Activity 2024-004-FA and this release only provides the UAS portion.