PUBLISHER: Polaris Market Research | PRODUCT CODE: 1719959
PUBLISHER: Polaris Market Research | PRODUCT CODE: 1719959
The global geospatial analytics market size is expected to reach USD 88.01 billion by 2034, according to a new study by Polaris Market Research. The report "Geospatial Analytics Market Size, Share, Trends, Industry Analysis Report: By Component (Software and Services), Type, Application, Technology, Data Type, and Region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa) - Market Forecast, 2025-2034" gives a detailed insight into current market dynamics and provides analysis on future market growth.
The geospatial analytics market involves the collection, analysis, and visualization of geographic data using GIS, AI, and big data to support decision-making across industries like transportation, agriculture, defense, and urban planning. The geospatial analytics market is evolving with advancements in spatial analysis, increasing smart city initiatives, and expanding applications across industries. A key trend shaping the geospatial analytics market expansion is the integration of artificial intelligence (AI) and machine learning (ML) into geospatial analytics, enhancing automation, predictive modeling, and decision-making capabilities. AI-driven geospatial solutions streamline complex spatial data processing, enabling organizations to extract actionable insights from vast datasets efficiently. This trend is particularly impactful in applications such as disaster response, environmental monitoring, and precision agriculture, where real-time analytics play a critical role in optimizing outcomes. The growing adoption of AI-powered geospatial tools underscores the market's shift toward intelligent, automated spatial intelligence solutions.
Another major trend driving geospatial analytics market growth is the rising adoption of cloud-based geospatial analytics platforms, enabling scalable, on-demand access to high-performance computing and spatial data processing. Cloud-based solutions enhance collaboration, interoperability, and data integration, allowing users to seamlessly analyze and visualize geospatial data from multiple sources. This shift toward cloud-based geospatial platforms is particularly beneficial for government agencies, enterprises, and research institutions that require large-scale spatial data analysis without extensive on-premise infrastructure. Therefore, as cloud adoption accelerates, it is expected to further enhance the efficiency and accessibility of geospatial analytics, fostering market expansion across various industries.
In terms of data type, the raster data segment led the expansion of the geospatial analytics market in 2024, driven by its ability to provide high-resolution spatial representation and its broad applicability in industries like agriculture and urban planning.
Based on technology, the ML & advanced analytics segment is expected to experience the fastest growth in the geospatial analytics market during the forecast period, fueled by the rising demand for automated data processing, predictive analytics, and real-time decision-making capabilities.
North America emerged as the leading region in the global market in 2024, attributed to the strong presence of technology-driven industries, significant investments in geospatial infrastructure, and the early adoption of advanced analytics technologies.
The Asia Pacific geospatial analytics market is projected to register the highest growth rate during the forecast period, supported by rapid urbanization, large-scale infrastructure development, and increasing implementation of smart city initiatives across the region.
A few global key market players include CSS Corp; Cybertech Systems and Software Ltd; Cyient; Esri; General Electric; Genesys International Corporation; GeoIQ; Google LLC; Hexagon India; Kentrix; Maxar Technologies; ML Infomap; NIIT Technologies (Coforge); Precisely; and Rolta India Ltd.
Polaris Market Research has segmented the geospatial analytics market report on the basis of component, type, application, technology, data type, and region: