The Cloud GIS market is experiencing robust growth, projected to reach $1513.8 million in 2025 and expanding at a Compound Annual Growth Rate (CAGR) of 17.2% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing adoption of cloud computing across various industries, coupled with the need for enhanced data accessibility and collaboration, is fueling demand for cloud-based Geographic Information Systems (GIS). Businesses are leveraging cloud GIS for improved operational efficiency, cost savings through reduced infrastructure needs, and streamlined data management. Furthermore, advancements in cloud-based GIS technologies, including enhanced analytical capabilities and integration with other enterprise systems, are contributing to market expansion. The accessibility and scalability offered by cloud platforms are proving particularly attractive to smaller businesses and organizations that previously lacked the resources to implement sophisticated GIS solutions. Competitive players like ESRI, Google Maps, Bing Maps, and others are continually innovating, introducing user-friendly interfaces and powerful analytics tools that further accelerate market adoption. The market segmentation reveals a dynamic landscape, with various industries utilizing cloud GIS for specific applications. While precise segment data is unavailable, we can infer strong growth in sectors like urban planning, environmental monitoring, and resource management, driven by the need for real-time data analysis and collaborative decision-making. Geographic variations in adoption rates are expected, with North America and Europe likely maintaining leading positions due to advanced technological infrastructure and early adoption. However, emerging economies in Asia and Latin America are expected to witness significant growth in the coming years as cloud infrastructure develops and awareness of cloud GIS benefits increases. While potential restraints such as data security concerns and internet connectivity challenges exist, the overall market outlook remains strongly positive, supported by continuous technological advancements and increasing industry adoption.

GIS in the Cloud Market Outlook

The GIS in the Cloud market is poised for significant growth, with a projected market size increasing from $3.2 billion in 2023 to $7.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 9.8%. This growth is primarily driven by the increasing adoption of cloud-based geographic information systems (GIS) across various industries. Factors such as cost efficiency, scalability, and ease of access to geospatial data are encouraging industries to shift from traditional GIS solutions to cloud-based platforms. Additionally, the surge in demand for real-time location data analytics and the proliferation of Internet of Things (IoT) devices further underpin the market's expansion.

One of the primary growth drivers for the GIS in the Cloud market is the increasing need for spatial data in various sectors. Industries such as agriculture and utilities rely heavily on geospatial data to enhance their operational efficiency and decision-making processes. The integration of AI and machine learning with cloud-based GIS has further amplified the capabilities of these systems, enabling more precise and automated data analysis. This technological synergy is propelling the demand for cloud GIS solutions, as businesses seek to harness advanced analytics for improved insights and competitive advantage. Furthermore, the rise of smart city initiatives globally is fueling the demand for GIS solutions hosted in the cloud, as urban planning and management increasingly rely on spatial analytics for sustainable development.

The transition from on-premises GIS to cloud-based solutions offers significant cost benefits, which is a major growth factor for the market. Cloud GIS solutions eliminate the need for expensive hardware and maintenance, allowing companies to allocate resources more efficiently. This cost-effectiveness is particularly appealing to small and medium enterprises (SMEs) that may lack substantial IT budgets. Moreover, the cloud's scalability allows organizations to adjust their GIS capabilities in line with their growth, avoiding the limitations of fixed-capacity systems. The flexibility and reduced total cost of ownership associated with cloud GIS are encouraging more businesses to adopt these solutions, boosting market growth.

Another critical factor driving the market's growth is the growing demand for real-time geospatial analytics. Modern businesses require instantaneous access to data to make timely and informed decisions. Cloud-based GIS platforms facilitate real-time data processing and sharing, providing organizations with up-to-the-minute insights into their operations and environments. This capability is particularly vital in sectors such as transportation and emergency services, where rapid response and decision-making are essential. The ability to leverage real-time data, combined with the global accessibility of cloud platforms, is significantly enhancing the value proposition of cloud GIS solutions.

Regionally, North America is expected to maintain its dominance in the GIS in the Cloud market, driven by the early adoption of advanced technologies and the presence of key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. Rapid urbanization, coupled with growing investments in smart city projects and infrastructure development, is fueling the demand for cloud GIS solutions in this region. Additionally, government initiatives aimed at enhancing digital infrastructure are further propelling the market. Both Europe and Latin America are also experiencing increased adoption of cloud GIS, driven by technological advancements and the need for efficient resource management in various industries.

Component Analysis

The component segment of the GIS in the Cloud market can be broadly categorized into software and services. The software component is a critical part of the market, which includes GIS platforms and applications that facilitate data visualization, spatial analysis, and mapping. The increasing demand for user-friendly and feature-rich GIS software is driving the growth of this segment. Advances in software functionalities, such as enhanced 3D visualization, real-time data processing, and AI-driven analytics, are making cloud-based GIS software more attractive to users. These advancements are helping organizations to derive more value from their spatial data, leading to higher adoption rates of GIS software solutions in the cloud environment.

On the services front, the market is witnessing a growing demand for professional

Elevation Point Cloud data is available from various sources. Visit the links below or contact Reed Burgette (reed.burgette@dogami.oregon.gov) at Department of Geology and Mineral Industries (DOGAMI) for more information. Resources: https://gis.dogami.oregon.gov/maps/lidarviewer/ ftp://lidar.engr.oregonstate.edu/ https://coast.noaa.gov/digitalcoast/ https://www.usgs.gov/programs/national-geospatial-program/national-map

Introduction

The Cloud GIS market is on a trajectory of robust growth, projected to reach a value of USD 3,303.1 Million by 2033, from USD 891 Million in 2023, with a compound annual growth rate (CAGR) of 14% during the forecast period spanning from 2024 to 2033. Cloud GIS, a technology leveraging cloud computing to manage geographic information system (GIS) data, is witnessing this expansion due to various factors, including the rising demand for real-time data access, the scalability of cloud services, and ongoing digital transformation efforts across industries.

The Cloud Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing adoption of cloud technologies across various sectors. This growth can be attributed to several factors, including the scalability, flexibility, and cost-effectiveness of cloud-based solutions. These systems enable users to store, manage, and analyze geographical data without substantial investment in hardware infrastructure, making GIS tools accessible to a broader range of industries and organizations.

However, the market faces challenges, notably concerns regarding data security and privacy. As geographic data often includes sensitive information, the potential for data breaches makes some organizations hesitant to adopt cloud-based GIS solutions. Moreover, the reliance on continuous internet connectivity can pose operational challenges in regions with unstable internet services.

Despite these challenges, the Cloud GIS market presents substantial opportunities for new entrants. The ongoing digital transformation and the expanding need for location-based data across sectors like urban planning, environmental monitoring, and transportation logistics create a fertile ground for innovative solutions. New players can differentiate themselves by offering enhanced security features, customized solutions, and robust offline capabilities to address existing market gaps.

This map contains a number of world-wide dynamic image services providing access to various Landsat scenes covering the landmass of the World for visual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. Newest and near cloud-free scenes are displayed by default on top. Most scenes collected since 1st January 2015 are included. The service also includes scenes from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).The service contains a range of different predefined renderers for Multispectral, Panchromatic as well as Pansharpened scenes. The layers in the service can be time-enabled so that the applications can restrict the displayed scenes to a specific date range. This ArcGIS Server dynamic service can be used in Web Maps and ArcGIS Desktop, Web and Mobile applications using the REST based image services API. Users can also export images, but the exported area is limited to maximum of 2,000 columns x 2,000 rows per request.Data Source: The imagery in these services is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The data for these services reside on the Landsat Public Datasets hosted on the Amazon Web Service cloud. Users can access full scenes from https://github.com/landsat-pds/landsat_ingestor/wiki/Accessing-Landsat-on-AWS, or alternatively access http://landsatlook.usgs.gov to review and download full scenes from the complete USGS archive.For more information on Landsat 8 images, see http://landsat.usgs.gov/landsat8.php.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit http://landsat.usgs.gov/science_GLS.php.For more information on each of the individual layers, see http://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b ; http://www.arcgis.com/home/item.html?id=6b003010cbe64d5d8fd3ce00332593bf ; http://www.arcgis.com/home/item.html?id=a7412d0c33be4de698ad981c8ba471e6

If you need information on how ArcGIS Online, or other cloud based Esri services work and how the data is secured, you are in the right place. The links described below should help you answer any data security and governance questions related to the use of ArcGIS Online at your university.

The Cloud GIS market is projected to grow exponentially, with a market size of $5,273 million in 2025 and an impressive CAGR of 16.8% during the forecast period of 2025-2033. This surge in growth is driven by advancements in cloud computing, the proliferation of smart city projects, and the growing adoption of location-based services. Enterprises and government agencies worldwide are increasingly leveraging Cloud GIS for enhanced data management, analysis, and visualization capabilities. The market is segmented based on type (SaaS, PaaS, IaaS), application (government, enterprises), and region.Key vendors include ESRI, Google Maps (Google), Bing Maps (Microsoft), SuperMap, Zondy Cyber Group, GeoStar, Hexagon Geospatial, CARTO, and GIS Cloud. North America is currently the largest market for Cloud GIS, followed by Europe and Asia Pacific. However, emerging markets in the Middle East and Africa and Asia Pacific are expected to witness significant growth in the coming years due to rising demand for GIS solutions in urban planning and infrastructure development.

This dataset represents future land use as depicted in the general/comprehensive plans of the jurisdictions in the Sun Cloud project area. These data were collected from the individual agencies and compiled into a single dataset with a standardized, region-wide land use classification applied. The original land use has also been captured in the attributes of these data. Data were collected Spring 2021.

Click here to access the data directly from the Illinois State Geospatial Data Clearinghouse. These lidar data are processed Classified LAS 1.4 files, formatted to 2,117 individual 2500 ft x 2500 ft tiles; used to create Reflectance Images, 3D breaklines and hydro-flattened DEMs as necessary. Geographic Extent: Lake county, Illinois covering approximately 466 square miles. Dataset Description: WI Kenosha-Racine Counties and IL 4 County QL1 Lidar project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a derived nominal pulse spacing (NPS) of 1 point every 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, U.S Survey Feet and vertical datum of NAVD88 (GEOID12B), U.S. Survey Feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 2,117 individual 2500 ft x 2500 ft tiles, as tiled Reflectance Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema. Ground Conditions: Lidar was collected April-May 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Ayers established a total of 66 ground control points that were used to calibrate the lidar to known ground locations established throughout the WI Kenosha-Racine Counties and IL 4 County QL1 project area. An additional 195 independent accuracy checkpoints, 116 in Bare Earth and Urban landcovers (116 NVA points), 79 in Tall Grass and Brushland/Low Trees categories (79 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data. Users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of its limitations. Acknowledgement of the U.S. Geological Survey would be appreciated for products derived from these data. These LAS data files include all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The raw point cloud is of good quality and data passes Non-Vegetated Vertical Accuracy specifications.Link Source: Illinois Geospatial Data Clearinghouse

The global Geographic Information System (GIS) Software market is experiencing robust growth, driven by increasing adoption across various sectors, including government, utilities, and transportation. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The rising need for precise location-based data analysis, coupled with advancements in cloud computing and big data technologies, is enabling the development of sophisticated and scalable GIS solutions. Furthermore, the integration of GIS with other technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), is opening new avenues for innovation and application. This leads to enhanced spatial data management, improved decision-making capabilities, and optimized resource allocation across diverse industries. Government initiatives promoting digital transformation and smart city development also contribute significantly to market growth. However, the market faces certain challenges. High initial investment costs for software and infrastructure, along with the need for skilled professionals to operate and maintain these systems, can hinder wider adoption, particularly among smaller organizations. Data security and privacy concerns associated with handling sensitive geospatial data also pose a significant restraint. Despite these limitations, the overall market outlook for GIS software remains highly positive, driven by the increasing reliance on location intelligence across a broad spectrum of industries and the continuous evolution of GIS technologies. The increasing availability of open-source GIS software is also expected to foster market growth, particularly in developing economies. By 2033, the market is projected to reach approximately $45 billion, signifying a substantial increase in market value and adoption.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into tree and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Trees is useful in applications such as high-quality 3D basemap creation, urban planning, forestry workflows, and planning climate change response.Trees could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Tree in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputThe model is trained with classified LiDAR that follows the LINZ base specification. The input data should be similar to this specification.Note: The model is dependent on additional attributes such as Intensity, Number of Returns, etc, similar to the LINZ base specification. This model is trained to work on classified and unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Wellington CityTesting dataset - Tawa CityValidation/Evaluation dataset - Christchurch City Dataset City Training Wellington Testing Tawa Validating ChristchurchModel architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.991200 0.975404 0.983239 High Vegetation 0.933569 0.975559 0.954102Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 80%, Test: 20%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-121.69 m to 26.84 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-15 to +15 Maximum points per block8192 Block Size20 Meters Class structure[0, 5]Sample resultsModel to classify a dataset with 5pts/m density Christchurch city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

This free and open source Web Map Context (WMC) Opener Client for ArcGIS Desktop enables opening an OGC WMC document for viewing and further analysis in ArcGIS ArcMap.

Classifying trees from point cloud data is useful in applications such as high-quality 3D basemap creation, urban planning, and forestry workflows. Trees have a complex geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.Using the modelFollow the guide to use the model. The model can be used with the 3D Basemaps solution and ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with the attributes: X, Y, Z, and Number of Returns.Note: This model is trained to work on unclassified point clouds that are in a projected coordinate system, where the units of X, Y, and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The provided deep learning model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification.This model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time and compute resources while improving accuracy. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block, and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following 2 classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThis model is expected to work well in all regions globally, with an exception of mountainous regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. Class Precision Recall F1-score Trees / High-vegetation (5) 0.975374 0.965929 0.970628Training dataThis model is trained on a subset of UK Environment Agency's open dataset. The training data used has the following characteristics: X, Y and Z linear unit meter Z range -19.29 m to 314.23 m Number of Returns 1 to 5 Intensity 1 to 4092 Point spacing 0.6 ± 0.3 Scan angle -23 to +23 Maximum points per block 8192 Extra attributes Number of Returns Class structure [0, 5]Sample resultsHere are a few results from the model.

Statewide 2016 Lidar points colorized with 2018 NAIP imagery as a scene created by Esri using ArcGIS Pro for the entire State of Connecticut. This service provides the colorized Lidar point in interactive 3D for visualization, interaction of the ability to make measurements without downloading.Lidar is referenced at https://cteco.uconn.edu/data/lidar/ and can be downloaded at https://cteco.uconn.edu/data/download/flight2016/. Metadata: https://cteco.uconn.edu/data/flight2016/info.htm#metadata. The Connecticut 2016 Lidar was captured between March 11, 2016 and April 16, 2016. Is covers 5,240 sq miles and is divided into 23, 381 tiles. It was acquired by the Captiol Region Council of Governments with funding from multiple state agencies. It was flown and processed by Sanborn. The delivery included classified point clouds and 1 meter QL2 DEMs. The 2016 Lidar is published on the Connecticut Environmental Conditions Online (CT ECO) website. CT ECO is the collaborative work of the Connecticut Department of Energy and Environmental Protection (DEEP) and the University of Connecticut Center for Land Use Education and Research (CLEAR) to share environmental and natural resource information with the general public. CT ECO's mission is to encourage, support, and promote informed land use and development decisions in Connecticut by providing local, state and federal agencies, and the public with convenient access to the most up-to-date and complete natural resource information available statewide.Process used:Extract Building Footprints from Lidar1. Prepare Lidar - Download 2016 Lidar from CT ECO- Create LAS Dataset2. Extract Building Footprints from LidarUse the LAS Dataset in the Classify Las Building Tool in ArcGIS Pro 2.4.Colorize LidarColorizing the Lidar points means that each point in the point cloud is given a color based on the imagery color value at that exact location.1. Prepare Imagery- Acquire 2018 NAIP tif tiles from UConn (originally from USDA NRCS).- Create mosaic dataset of the NAIP imagery.2. Prepare and Analyze Lidar Points- Change the coordinate system of each of the lidar tiles to the Projected Coordinate System CT NAD 83 (2011) Feet (EPSG 6434). This is because the downloaded tiles come in to ArcGIS as a Custom Projection which cannot be published as a Point Cloud Scene Layer Package.- Convert Lidar to zlas format and rearrange. - Create LAS Datasets of the lidar tiles.- Colorize Lidar using the Colorize LAS tool in ArcGIS Pro. - Create a new LAS dataset with a division of Eastern half and Western half due to size limitation of 500GB per scene layer package. - Create scene layer packages (.slpk) using Create Cloud Point Scene Layer Package. - Load package to ArcGIS Online using Share Package. - Publish on ArcGIS.com and delete the scene layer package to save storage cost.Additional layers added:Visit https://cteco.uconn.edu/projects/lidar3D/layers.htm for a complete list and links. 3D Buildings and Trees extracted by Esri from the lidarShaded Relief from CTECOImpervious Surface 2012 from CT ECONAIP Imagery 2018 from CTECOContours (2016) from CTECOLidar 2016 Download Link derived from https://www.cteco.uconn.edu/data/download/flight2016/index.htm

A listing of web services published from the authoritative East Baton Rouge Parish Geographic Information System (EBRGIS) data repository. Services are offered in Esri REST, and the Open Geospatial Consortium (OGC) Web Mapping Service (WMS) or Web Feature Service (WFS) formats.

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market value of approximately $45 billion by 2033. Key drivers include the rising adoption of cloud-based GIS solutions, enhanced data analytics capabilities, the proliferation of location-based services, and the growing need for precise spatial data analysis in various industries like urban planning, geological exploration, and water resource management. The market is segmented by application (Geological Exploration, Water Conservancy Projects, Urban Planning, Others) and type (Cloud-based, Web-based). Cloud-based solutions are gaining significant traction due to their scalability, accessibility, and cost-effectiveness. The increasing availability of high-resolution satellite imagery and advancements in artificial intelligence (AI) and machine learning (ML) are further fueling market expansion. While data security concerns and the high initial investment costs for some advanced solutions present restraints, the overall market outlook remains positive, with significant opportunities for both established players and emerging technology providers. Geographical expansion is another key aspect of market growth. North America and Europe currently hold a significant market share, owing to established GIS infrastructure and early adoption of advanced technologies. However, the Asia-Pacific region is expected to witness rapid growth in the coming years, driven by rising government investments in infrastructure development and increasing urbanization in countries like China and India. Competitive dynamics are shaping the market, with major players like Esri, Autodesk, Hexagon, and Mapbox competing on the basis of software features, data integration capabilities, and customer support. The emergence of open-source GIS solutions like QGIS and GRASS GIS is also challenging the dominance of proprietary software, offering cost-effective alternatives for various applications. The continued development and integration of advanced technologies like 3D mapping, real-time data visualization, and location intelligence will further enhance the capabilities of GIS mapping tools, driving market expansion and innovation across various sectors.

The global GIS in the Cloud market is projected to reach $1528 million by 2033, exhibiting a CAGR of 17.2% during 2025-2033. The market has witnessed steady growth due to factors such as the increasing adoption of cloud computing, advancements in GIS technology, and the need for real-time data analysis. The market is segmented based on application (government, enterprises) and type (SaaS, PaaS, IaaS). Key drivers of the GIS in the Cloud market include the growing need for geospatial data, the increasing adoption of mobile GIS applications, and the rising demand for real-time data analysis. Major trends in the market include the integration of GIS with other technologies such as IoT and AI, the development of new cloud-based GIS platforms, and the increasing use of open source GIS software. The market is also expected to benefit from the increasing adoption of cloud computing in emerging economies, as well as the growing demand for GIS services in sectors such as natural resource management, urban planning, and disaster response.

ArcGIS Location Tracking Privacy Best Practices (Esri Whitepaper).This document contains relevant information that helps guide IT managers, GIS administrators, andprivacy and security team members in deploying cloud and enterprise GIS in a manner that helps complywith privacy regulations, such as GDPR, for location tracking services._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Become an ArcGIS Hub Specialist.ArcGIS Hub is a cloud-based engagement platform that helps organizations work more effectively with their communities. Learn how to use ArcGIS Hub capabilities and related technology to coordinate and engage with external agencies, community partners, volunteers, and citizens to tackle the projects that matter most in your community._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...