Tagged image tiles as well as the Faster-RCNN framework for automatic extraction of road intersection points from USGS historical maps of the United States of America. The data and code have been prepared for the paper entitled "Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks" submitted to "International Journal of Geographic Information Science". The image tiles have been tagged manually. The Faster RCNN framework (see https://arxiv.org/abs/1611.10012) was captured from:https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

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GIS In Telecom Sector Market Size 2025-2029

The GIS in telecom sector market size is forecast to increase by USD 2.35 billion at a CAGR of 15.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of Geographic Information Systems (GIS) for capacity planning in the telecommunications industry. GIS technology enables telecom companies to optimize network infrastructure, manage resources efficiently, and improve service delivery. Telecommunication assets and network management systems require GIS integration for efficient asset management and network slicing. However, challenges persist in this market. A communication gap between developers and end-users poses a significant obstacle.
Companies seeking to capitalize on opportunities in the market must focus on addressing these challenges, while also staying abreast of technological advancements and market trends. Effective collaboration between developers and end-users, coupled with strategic investments, will be essential for success in this dynamic market. Telecom companies must bridge this divide to ensure the development of user-friendly and effective GIS solutions. Network densification and virtualization platforms are key trends, allowing for efficient spectrum management and data monetization. Additionally, the implementation of GIS in the telecom sector requires substantial investment in technology and infrastructure, which may deter smaller players from entering the market.

What will be the Size of the GIS In Telecom Sector 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.
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In the dynamic telecom sector, GIS technology plays a pivotal role in customer analysis, network planning, and infrastructure development. Customer experiences are enhanced through location-based services and real-time data analysis, enabling telecom companies to tailor offerings and improve service quality. Network simulation and capacity planning are crucial for network evolution, with machine learning and AI integration facilitating network optimization and compliance with industry standards.
IOT connectivity and network analytics platforms offer valuable insights for smart city infrastructure development, with 3D data analysis and network outage analysis ensuring network resilience. Telecom industry partnerships foster innovation and collaboration, driving the continuous evolution of the sector. Consulting firms offer expertise in network compliance and network management, ensuring regulatory adherence and optimal network performance.

How is this GIS In Telecom Sector Industry segmented?

The gis in telecom sector 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.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Application

  Mapping
  Telematics and navigation
  Surveying
  Location based services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period. In the telecom sector, the deployment of 5G networks is driving the need for advanced Geographic Information Systems (GIS) to optimize network performance and efficiency. GIS technology enables spatial analysis, network automation, capacity analysis, and bandwidth management, all crucial elements in the rollout of 5G networks. Large enterprises and telecom consulting firms are integrating GIS data into their operations for network planning, optimization, and troubleshooting. Machine learning and artificial intelligence are transforming GIS applications, offering predictive analytics and real-time network performance monitoring. Network virtualization and software-defined networking are also gaining traction, enhancing network capacity and improving network reliability and maintenance.

GIS software companies provide solutions for desktops, mobiles, cloud, and servers, catering to various industry needs. Smart city initiatives and location-based services are expanding the use cases for GIS in telecom, offering new opportunities for growth. Infrastructure deployment and population density analysis are critical factors in network rollout and capacity enhancement. Network security and performance monitoring are essential components of GIS applications, ensuring network resilience and customer experience management. Edge computing and network latency reduction are also signi

GIS in Telecom Sector Market Outlook

The global GIS in telecom sector market size was valued at approximately USD 1.7 billion in 2023 and is projected to reach USD 4.5 billion by 2032, growing at a CAGR of 11.5% during the forecast period. This substantial growth is driven by the increasing demand for advanced mapping and analysis tools in the telecom industry, which plays a crucial role in enhancing network performance, managing assets, and optimizing location-based services. The rapid technological advancements in geospatial data processing and the increasing integration of GIS with IoT, 5G, and AI technologies further contribute to the market’s expansion.

The growth factors for the GIS in telecom sector market are multifaceted and robust. The primary driver is the rising demand for enhanced customer experience and network efficiency, which GIS technology offers through precise mapping and real-time data analytics. Telecom operators are increasingly adopting GIS to optimize their network management processes, reduce operational costs, and improve service delivery. Additionally, the burgeoning demand for location-based services and the growing utilization of GIS in planning and deploying 5G networks are significant contributors to market growth. These applications are essential for telecom companies seeking to expand their networks and enhance connectivity, especially in rural and underserved areas.

The integration of GIS with emerging technologies such as IoT and AI is also a critical growth driver in this market. As telecom companies strive to offer more personalized and efficient services, the role of GIS in analyzing large volumes of geospatial data becomes vital. This integration facilitates better decision-making processes, enabling telecom operators to tailor their services according to specific geographic and demographic needs. Furthermore, GIS technology provides significant cost benefits by optimizing asset management and ensuring more efficient use of resources, which is increasingly appealing in a competitive market landscape.

Another growth factor is the increasing regulatory mandates and policies aimed at improving telecom infrastructure. Governments across the globe are investing heavily in modernizing telecom networks, and GIS plays a crucial role in these initiatives. By providing comprehensive spatial data and analytics, GIS technology assists in the strategic planning and deployment of telecom infrastructure, ensuring compliance with regulatory standards. Moreover, the rise in smart city projects, which rely heavily on advanced telecom networks, further propels the demand for GIS solutions in the telecom sector.

Regionally, North America dominates the GIS in telecom sector market due to its early adoption of advanced technologies and significant investments in telecom infrastructure. The presence of major telecom companies and technology providers also contributes to the region's leading position. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid expansion of telecom networks and the increasing focus on digital transformation initiatives. Emerging economies in this region are investing heavily in 5G deployment and smart city projects, which boost the demand for GIS solutions. Europe and Latin America also present significant opportunities for market growth, with ongoing investments in network modernization and digital infrastructure development.

Component Analysis

The GIS in telecom sector market is segmented into software, hardware, and services, each playing a pivotal role in the industry’s development. The software segment, which includes GIS mapping and analytics tools, is expected to hold the largest market share. This is attributed to the increasing demand for advanced software solutions that enable telecom operators to analyze geospatial data for network optimization and strategic planning. The continuous evolution of software capabilities, such as real-time analytics and cloud-based services, further propels the demand for GIS software in the telecom sector.

Hardware components, which include GPS devices, GNSS receivers, and other geospatial data collection tools, are crucial for data acquisition in GIS applications. Although this segment may not be as large as the software segment, its importance cannot be overstated. Advances in hardware technology have significantly improved data accuracy and processing speeds, enabling telecom companies to efficiently collect and analyze large volumes of geospatial data. The increasing integration of these hardwar

INFP, CRMD and UCL have developed a framework capable of analyzing the implications of natural hazards on transportation networks, also in a time-dependent manner. This is currently embedded into an ArcGIS toolbox entitled Network-risk, which has been successfully tested for Bucharest, contributing to an insightful evaluation of emergency intervention times for ambulances and firefighters, in the case of an earthquake. The files and the user manual allow a replication of our recent analysis in Toma-Danila et al. (2022) and a download of results (such as affected roads and unaccesible areas in Bucharest), in various formats. Some of the results are also presented in an ArcGIS Online app, called "Riscul seismic al Bucurestiului" (The seismic risk of Bucharest), available at https://tinyurl.com/yt32aeyx. In the files you can find: - the Bucharest road network used in the article; - facilities for Bucharest and Ilfov, such as hospitals, firestations, buildings with seismic risk or tramway lines accesible by emergency vehicles - results of the analysis: unaccesible roads and areas, service areas around facilities, closest facilities for representative points - Excel calculator for Z elevation from OpenStreetMap data - the user manual and a ArcGIS toolbox.

Main citation: - Toma-Danila D., Tiganescu A., D'Ayala D., Armas I., Sun L. (2022) Time-Dependent Framework for Analyzing Emergency Intervention Travel Times and Risk Implications due to Earthquakes. Bucharest Case Study. Frontiers in Earth Science, https://doi.org/10.3389/feart.2022.834052

Previous references: - Toma-Danila D., Armas I., Tiganescu A. (2020) Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania. Natural Hazards and Earth System Sciences, 20(5): 1421-1439, https://doi.org/10.5194/nhess-20-1421-2020 - Toma-Danila D. (2018) A GIS framework for evaluating the implications of urban road network failure due to earthquakes: Bucharest (Romania) case study. Natural Hazards, 93, 97-111, https://link.springer.com/article/10.1007/s11069-017-3069-y

The Intelligent Road Network dataset provided by the Transport Department includes traffic directions, turning restrictions at road junctions, stopping restrictions, on-street parking spaces and other road traffic data for supporting the development of intelligent transport system, fleet management system and car navigation etc. by the public.

Esri China (HK) has prepared this File Geodatabase containing a Network Dataset for the Intelligent Road Network to support Esri GIS users to use the dataset in ArcGIS Pro without going through long configuration steps. Please refer to this guideline to use the Road Network Dataset in ArcGIS Pro for routing analysis. This network dataset has been configured and deployed the following restrictions:

Speed LimitTurnIntersectionTraffic FeaturesPedestrian ZoneTraffic Sign of ProhibitionVehicle RestrictionThe coordinate system of this dataset is Hong Kong 1980 Grid.The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Transport Department at https://data.gov.hk/en-data/dataset/hk-td-tis_15-road-network-v2.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.Dataset last updated on: 2021 July

This FeatureClass is one of a set of FeatureClasses derived from our main FeatureClass (STREETS), which were created to display information related to our County's streets. This set of derivative products includes the following FeatureClasses: An Arc FeatureClass of Miami-Dade County streets with seamless address ranges. An Arc FeatureClass of Miami-Dade County streets with actual address ranges. An Arc FeatureClass of Miami-Dade County streets in a Coverage Structure. A Polygon FeatureClass of Miami-Dade County streets created from the street base layer using a process called 'buffer'. It was designed for cartographic display purposes and does not have attributes.Updated: Weekly-Sat

InputData DirectoryThis network dataset is an example of a network to which paths from other networks (i.e. Networks A and B) can be compared.Contains two directories:a) NetworkCb) NetworkPaths'NetworkC' Directory- This network is based upon a subset of the Missouri Department of Transportation (MoDOT) July 2016 road dataset listed in the references.- NetworkC contains an ESRI .gdb (NetworkCdata.gdb) in which the arcs and nodes for Network C can be found as well as an ArcGIS ND Network Analyst configuration file. - Featuredataset: NetworkCsub - Network arcs: NetworkCsub - Network file: NetworkCsub_ND - Network junctions: NetworkCsub_ND_Junctions'NetworkPaths' contains ESRI .gdbs representing:a) A collection of routes between OD pairs in each network (InputPaths.gdb) - The densified routes used in the application (densified at 10m): (Net_A_routelines; Net_B_routelines; Net_C_routelines) - The original routes with original set of vertices (non densified): (Net_A_routes; Net_B_routes; Net_C_routes)b) The origin and destination points for the paths (ODNodes.gdb) - These were used to generate the shortest paths for each network, serving as the paths to be compared - origins: originLocations - destinations: destinationLocations_'OutputData' DirectoryContains the comparisons of paths to networks:NetAToB: comparison of paths from network A to network BNetAToC: comparison of paths from network A to network CNetBToA: comparison of paths from network B to network ANetBToC: comparison of paths from network B to network CNetCToA: comparison of paths from network C to network ANetCToB: comparison of paths from network C to network BInside each directory is a collection of ESRI .gdb which contains the individual paths used in the analysis as inputa) NetworkAPaths.gdbb) NetworkBPaths.gdbc) NetworkCPaths.gdbInside each directory is a collection of ESRI .gdb which contains the vertices of the individual paths used in the analysis as inputa) NetworkAPathPoints.gdbb) NetworkBPathPoints.gdbc) NetworkCPathPoints.gdbAlso included is a collection of ESRI .gdb that represent the original path nodes that could be assigned to the comparison network. In this case, only nodes that were within 20m of the comparison network could be assigned. Each path node is attributed with the distance to its counterpart node in the comparison. a) Nodes in Network A paths assigned to Network B (PathANodesAssignedtoNetB.gdb)b) Nodes in Network A paths assigned to Network C (PathANodesAssignedtoNetC.gdb)c) Nodes in Network B paths assigned to Network A (PathBNodesAssignedtoNetA.gdb)d) Nodes in Network B paths assigned to Network C (PathBNodesAssignedtoNetC.gdb)e) Nodes in Network C paths assigned to Network A (PathCNodesAssignedtoNetA.gdb)f) Nodes in Network C paths assigned to Network B (PathCNodesAssignedtoNetB.gdb)Inside each directory is a collection of ESRI .gdb which contain solutions to the SCPPOD with the following naming convention:a) comparing paths in Network A to Network B SCCPODarcsPathAtoNetB.gdb for arc elements and SCCPODnodesPathAtoNetB.gdb for node elements) - The naming convention for the node solutions for path id X is ('SN_routeX_X') - The naming convention for the arc solutions for path id X is ('routX_Rt' for single polyline counterpart path; and 'routeX_Rtsplit' for a polyline representation of the counterpart path based upon the SCPPOD node output).b) comparing paths in Network A to Network C SCCPODarcsPathAtoNetC.gdb for arc elements and SCCPODnodesPathAtoNetC.gdb for node elements)c) comparing paths in Network B to Network A SCCPODarcsPathBtoNetA.gdb for arc elements and SCCPODnodesPathBtoNetA.gdb for node elements)d) comparing paths in Network B to Network C SCCPODarcsPathBtoNetC.gdb for arc elements and SCCPODnodesPathBtoNetC.gdb for node elements)e) comparing paths in Network C to Network A SCCPODarcsPathCtoNetA.gdb for arc elements and SCCPODnodesPathCtoNetA.gdb for node elements)f) comparing paths in Network C to Network B SCCPODarcsPathCtoNetB.gdb for arc elements and SCCPODnodesPathCtoNetB.gdb for node elements)The counterpart paths that were identified were then linked to the full network C to summarize the frequency with with arcs were associated with paths - Can be found in: 1. PathARepresentationinNetC.gdb 2. PathARepresentationinNetC.gdb - important attributes: a) vcntarc: number of paths utilizing arc b) ptCnt: number of path vertices associated with each arc c) AvgDist: average distance of path vertices from network arcs d) MinDist: minimum distance of path vertices from network arcs e) MaxDist: minimum distance of path vertices from network arcs

Features of different accessibility analysis methods.

Detailed, building -specific assessment of indoor mobile signal strength and propagation across all licensed mobile operators in a given country. Signal values are provided for each H3-12 hexagon inside the building (resolution approx. 20 x 20 meters). The data is presented in GIS-compatible formats such as gpkg and geojson. The data is obtained using crowdsourced data and advanced geo-spatial algorithms and includes data on the presence of indoor coverage systems. This data can be purchased on a building-by-building basis

Typical data use cases are in the following sectors: - B2B telecommunications: assess indoor coverage quality to optimise deployment of mobile-dependent network services (e.g. SD-WAN, mobile backup, etc..). - Mobile telecoms: Mobile operators and indoor coverage solution providers (e.g. DAS providers) can use this data to identify buildings and building owners for the deployment of indoor coverage systems - Commercial real estate and property: ascertain the quality of indoor mobile coverage to ensure that tenants can actually conduct business in your premises

The USDA Long-Term Agroecosystem Research was established to develop national strategies for sustainable intensification of agricultural production. As part of the Agricultural Research Service, the LTAR Network incorporates numerous geographies consisting of experimental areas and locations where data are being gathered. Starting in early 2019, two working groups of the LTAR Network (Remote Sensing and GIS, and Data Management) set a major goal to jointly develop a geodatabase of LTAR Standard GIS Data Layers. The purpose of the geodatabase was to enhance the Network's ability to utilize coordinated, harmonized datasets and reduce redundancy and potential errors associated with multiple copies of similar datasets. Project organizers met at least twice with each of the 18 LTAR sites from September 2019 through December 2020, compiling and editing a set of detailed geospatial data layers comprising a geodatabase, describing essential data collection areas within the LTAR Network. The LTAR Standard GIS Data Layers geodatabase consists of geospatial data that represent locations and areas associated with the LTAR Network as of late 2020, including LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This geodatabase was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. The creation of the geodatabase began with initial requests to LTAR site leads and data managers for geospatial data, followed by meetings with each LTAR site to review the initial draft. Edits were documented, and the final draft was again reviewed and certified by LTAR site leads or their delegates. Revisions to this geodatabase will occur biennially, with the next revision scheduled to be published in 2023. Resources in this dataset:Resource Title: LTAR Standard GIS Data Layers, 2020 version, File Geodatabase. File Name: LTAR_Standard_GIS_Layers_v2020.zipResource Description: This file geodatabase consists of authoritative GIS data layers of the Long-Term Agroecosystem Research Network. Data layers include: LTAR site locations, LTAR site points of contact and street addresses, LTAR experimental boundaries, LTAR site "legacy region" boundaries, LTAR eddy flux tower locations, and LTAR phenocam locations.Resource Software Recommended: ArcGIS,url: esri.com Resource Title: LTAR Standard GIS Data Layers, 2020 version, GeoJSON files. File Name: LTAR_Standard_GIS_Layers_v2020_GeoJSON_ADC.zipResource Description: The contents of the LTAR Standard GIS Data Layers includes geospatial data that represent locations and areas associated with the LTAR Network as of late 2020. This collection of geojson files includes spatial data describing LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This dataset was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. Resource Software Recommended: QGIS,url: https://qgis.org/en/site/

Overview:This document describes the 2021 accessibility data released by the Accessibility Observatory at the University of Minnesota. The data are included in the National Accessibility Evaluation Project for 2021, and this information can be accessed for each state in the U.S. at https://access.umn.edu/research/america. The following sections describe the format, naming, and content of the data files.Data Formats: The data files are provided in a Geopackage format. Geopackage (.gpkg) files are an open-source, geospatial filetype that can contain multiple layers of data in a single file, and can be opened with most GIS software, including both ArcGIS and QGIS.Within this zipfile, there are six geopackage files (.gpkg) structured as follows. Each of them contains the blocks shapes layer, results at the block level for all LEHD variables (jobs and workers), with a layer of results for each travel time (5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 minutes). {MPO ID}_tr_2021_0700-0859-avg.gpkg = Average Transit Access Departing Every Minute 7am-9am{MPO ID}_au_2021_08.gpkg = Average Auto Access Departing 8am{MPO ID}_bi_2021_1200_lts1.gpkg = Average Bike Access on LTS1 Network{MPO ID}_bi_2021_1200_lts2.gpkg = Average Bike Access on LTS2 Network{MPO ID}_bi_2021_1200_lts3.gpkg = Average Bike Access on LTS3 Network{MPO ID}_bi_2021_1200_lts4.gpkg = Average Bike Access on LTS4 NetworkFor mapping and geospatial analysis, the blocks shape layer within each geopackage can be joined to the blockid of the access attribute data. Opening and Using Geopackages in ArcGIS:Unzip the zip archiveUse the "Add Data" function in Arc to select the .gpkg fileSelect which layer(s) are needed — always select "main.blocks" as this layer contains the Census block shapes; select any other attribute data layers as well.There are three types of layers in the geopackage file — the "main.blocks" layer is the spatial features layer, and all other layers are either numerical attribute data tables, or the "fieldname_descriptions" metadata layer. The numerical attribute layers are named with the following format:[mode]_[threshold]_minutes[mode] is a two-character code indicating the transport mode used[threshold] is an integer indicating the travel time threshold used for this data layerTo use the data spatially, perform a join between the "main.blocks" layer and the desired numerical data layer, using either the numerical "id" fields, or 15-digit "blockid" fields as join fields.

ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

The v25 download is available for use and valid as of 12/31/2024. Due to issues with other annual processes, Roadsoft and other MDOT systems are not scheduled for v25. Please be aware if you update your data to use this v25 centerline info, it may not align with Roadsoft and other MDOT systems at this time.The Michigan Department of Transportation (MDOT) manages several Linear Referencing Systems (LRS) for the State including the Physical Road (PR), Control Section (CS) and Route networks These networks include Michigan's Roads, Rails, Trails & more. MDOT also partners with the Michigan State Police (MSP), Department of Natural Resources (DNR), Department of Technology Management & Budged (DTMB) and others to manage the Michigan LRS.For more information, please view the Michigan Linear Referencing Home Page.

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

The global Geographic Information System (GIS) in Telecom market is expected to reach $1092.7 million by 2033, expanding at a CAGR of 3.0% from 2025 to 2033. Drivers of the market include the increasing adoption of GIS in network planning and optimization, asset management, and customer relationship management. Cloud-based GIS solutions are gaining traction due to their cost-effectiveness and scalability. Large enterprises are expected to dominate the market segment due to their complex infrastructure and data management requirements. Key players in the GIS market for Telecom include Esri, Hexagon, Trimble, and Pitney Bowes. North America is expected to hold the largest market share due to the presence of major telecom companies and the early adoption of GIS technologies. The Asia Pacific region is projected to exhibit the fastest growth rate due to the rapid expansion of the telecom industry in countries such as China and India. Telecommunication companies utilize GIS to optimize network planning and automate asset management, resulting in improved efficiency and cost savings. The emergence of 5G and IoT is creating new opportunities for GIS in telecom, driving market growth in the coming years. The global Geographic Information System (GIS) in Telecom market is projected to reach $20 billion by 2026, growing at a CAGR of 9.2% from 2021 to 2026. The market is driven by the increasing demand for location-based services, the need for improved network planning and optimization, and the rise of smart cities.

According to Cognitive Market Research, the global GIS in Telecom market size is USD 1658.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 13.30% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 663.2 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.5% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 497.4 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 381.3 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.3% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 82.9 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.7% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 33.1 million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.0% from 2024 to 2031.
The Data Offering held the highest GIS in Telecom market revenue share in 2024.

Market Dynamics of GIS in Telecom Market

Key Drivers for GIS in Telecom Market

Rapid Surge in Demand for Network Installation to Increase the Demand Globally

The global demand for network installation is a key driver of the GIS in Telecom market. Determining the optimal location for network towers is essential for efficient operations. With increasing competition and the rapid deployment of new wireless technologies like 4G and 5G, telecom companies are prioritizing network planning and targeted expansion. According to the GSMA's Mobile Economy Report, the number of 5G global connections is expected to reach one billion in 2022 and double to two billion by 2025, representing a quarter of all mobile connections. Ericsson's Q4 2021 Mobility Report also highlighted this trend, raising its global forecast for 5G mobile connections from 580 million to 660 million by the end of 2021, driven by strong demand in China and North America. Given the high costs associated with planning, developing, and testing wireless networks, telecom companies are increasingly adopting GIS solutions to enhance their geographical data analysis for tower installation.

Rising Investments in Infrastructure Development to Propel Market Growth

Governments around the world are investing in smart city initiatives that require robust telecom infrastructure. Currently, 56% of the global population, or 4.4 billion people, reside in urban areas, and this figure is projected to rise to 68% over the next 30 years. By 2050, approximately 60% of the world's population is expected to be part of smart city initiatives. Consumer spending on smart home systems is predicted to reach $123 billion, while technology spending on smart city initiatives is anticipated to be around $124 billion. GIS is essential for planning and deploying these infrastructures. Additionally, there is a growing emphasis on extending telecom services to rural and remote areas, where GIS aids in the efficient planning and implementation of these connectivity programs.

Restraint Factor for the GIS in Telecom Market

High Initial Investment and Maintenance Costs to Limit the Sales

Deploying GIS technology involves substantial initial investment in software, hardware, and skilled personnel. This can be a significant barrier, especially for small and medium-sized telecom companies. The costs associated with maintaining and updating GIS systems, including data updates, system upgrades, and staff training, can be high and ongoing. GIS in telecom involves handling a large amount of sensitive geographic and customer data. Ensuring the security and privacy of this data is crucial, and any breaches can lead to significant financial and reputational damage..

Impact of Covid-19 on the GIS in Telecom Market

The shift to remote work and online education during the pandemic significantly increased the demand for reliable internet connectivity. Telecom companies had to expand and optimize their networks to meet this demand, driving the need for GIS solutions for efficient network planning and management. The rise in telemedicine and e-commerce also contributed to the increased demand for robust telecom infrastructure. GIS played a crucial rol...

This is a repository of the raw GIS data files that constitute the network mapping taken forward within the project.

The datasets are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/ The transmission line geojson and zipped shapefiles contain existing, planned, under construction lines. The source link: http://data-energy-gis.opendata.arcgis.com/datasets/6db06d51b0a34c9b989fc54c0d25c092_0 The substation geojson and zipped shapefiles contain existing, planned, under construction substations. The source link: http://data-energy-gis.opendata.arcgis.com/datasets/a7ef2af5ca9249babc5b20602edaba59_0 The transmission and substation datasets were last updated on March 9 2017.

Data file name: Beijing.rar Data deion: 1) after finishing public issued of Beijing city traffic figure, and Beijing map, and Beijing Tourism figure, by geometry corrected, and image distribution associate, work Hou, on the year road center line for vector quantitative, on vector quantitative of network data for edit, until network full, get has Beijing city five ring within, each 10 years around time interval of network GIS data, established has Beijing history network data set. 2) data file contains years of Beijing's road network data and route data is shapefile files and named for years (1969, 1978, 1990, 2000 and 2008). 3) shapefile file's property sheet for each year, the field "year_" section belongs to the year, the field "From_" indicates that this stretch of road network from previous vintages in the sections corresponding to the FID.

If you have any questions, please contact lianggao@bjtu.edu.CN.

The Geographic Information System (GIS) in Telecom sector market is experiencing robust growth, projected to reach a market size of $1.94 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 14.68% from 2025 to 2033. This expansion is driven by the increasing need for precise network planning and optimization within the telecom industry. The rising adoption of 5G and the Internet of Things (IoT) necessitates advanced GIS solutions for efficient infrastructure deployment, managing assets, and ensuring optimal network performance. Furthermore, the integration of GIS with big data analytics and AI capabilities allows telecom providers to analyze network data more effectively, predicting and preventing outages, optimizing resource allocation, and enhancing customer experiences. The market is segmented by product (Software, Data, Services) and deployment (On-premises, Cloud), with cloud-based solutions witnessing significant growth due to their scalability, cost-effectiveness, and accessibility. Leading players like Esri, Autodesk, and Bentley Systems are driving innovation through strategic partnerships and product advancements. However, challenges remain, including the high initial investment cost for implementing GIS systems and the need for skilled professionals capable of utilizing these sophisticated technologies. The North American and APAC regions are anticipated to dominate the market, driven by higher adoption rates and robust telecom infrastructure development. The European market is also projected for steady growth, driven by increasing investments in digital transformation within the telecommunication sector. The forecast period (2025-2033) anticipates continued market expansion, fueled by the escalating demand for advanced network management capabilities. The increasing complexity of telecom networks, the expansion of smart city initiatives, and the growing penetration of mobile devices will drive further adoption of GIS solutions. Competition within the market is intensifying, with companies focusing on delivering innovative solutions, strategic partnerships, and expanding their geographic reach. The market's growth trajectory will depend on factors like government initiatives supporting digital infrastructure development, the pace of 5G rollout, and the level of investment in advanced analytics and AI within the telecom industry. Overall, the GIS in Telecom sector presents a lucrative opportunity for market participants willing to adapt to changing technologies and customer demands.