Geospatial Trends in 2025: The Latest Industry Evolutions
The geospatial industry, much like the broader tech sector, is undergoing rapid innovations that are reshaping how businesses tackle challenges.
Generative AI is streamlining geospatial analysis, cloud data warehouses interoperability has significantly improved, Data as a Service (DaaS) is making geospatial data more accessible than ever, raster processing is making a comeback, and the demand for high-definition road data is increasing to support connected and autonomous vehicles.
Let’s explore those trends and the great possibilities they offer.
When Generative AI Meets Geospatial Technology
Generative AI (GenAI) is no longer just a buzzword, transitioning from hype to reality across industries. In the geospatial sector, GenAI’s outcomes are proving to be a significant productivity booster. Whether it’s generating code, analyzing data, summarizing trends, or enabling predictive analytics, GenAI is transforming the way we interact with spatial data.
One notable tool is Conversational GIS, where GenAI facilitates natural language interactions with maps and analytics. Companies like Carto are leading the way, with tools like their AI-powered assistant simplifying the process of extracting meaningful insights from geospatial analysis. This innovation opens the door to more intuitive and accessible geospatial workflows.
Despite these advancements, the journey from innovation to implementation is fraught with challenges. Many artificial intelligence projects are stuck in the proof-of-concept (POC) stage, with nearly 80% failing to progress into production. This high failure rate stems from several factors:
- Data lacking quality and context
- Poor change management
- Lack of clear return on investment (ROI)
- Shadow AI operating outside governance frameworks
The most common problem lies in data integrity, lacking the quality and context needed to make it AI-ready. Without trusted and contextual data, AI systems risk generating inaccurate results—false positives, false negatives, or even AI hallucinations.
For instance, data integrity is critical in geoAI applications, such as using computer vision to extract features from satellite imagery. Companies often overlook the importance of precise geocoding, which serves as the operational link between GeoAI processes and customer information. Data inaccuracies can lead to costly errors.
By embedding geospatial context and their internal business data into AI models, companies can enhance the reliability and resilience of their outputs. Advanced techniques exist for integrating geospatial context into AI workflows:
- Retrieval-Augmented Generation (RAG): Enhancing AI outputs with real time, relevant data.
- Dynamic Prompt Generation: Adapting prompts based on geospatial parameters.
- Session Context Supplementation: Incorporating spatial data context dynamically throughout an AI session.
The new Snowflake Cortex AI expands the possibilities by enabling businesses to quickly analyze data and build generative AI applications using fully managed large language models (LLMs), vector search, and fully managed text-to-SQL services.
Precisely and AWS have developed a simple integration, using LLM’s with Retrieval-Augmented Generation (RAG), showcasing how geospatial context enhances the quality of GenAI outcomes.
Data as a Service (DaaS) is Gaining Momentum
The scope of data offerings continues to broaden, driven by advancements in technology and growing business needs. Data enrichment and interoperability have become cornerstones of business processes. The rise of Data as a Service (DaaS) platforms have ushered in new capabilities, from dynamic data retrieval APIs to geospatial data integration with advanced formats like GeoIceberg and GeoParquet.
API-based DaaS solutions are leading the way, as seen with Vexcel’s Property Attributes API, enabling businesses to access on-demand data, such as high-quality imagery and automated feature extraction. These types of solutions address the evolving needs of businesses by providing dynamic, scalable data consumption that enhances efficiency while maintaining cost effectiveness.
Similarly, Precisely’s Data Graph API provides the ability to retrieve an unlimited number of attributes from hundreds of underlying datasets through a single, high-performance API call. This technology streamlines the enrichment process, minimizing integration complexities and delivering actionable insights faster.
Precisely’s Data Integrity Suite Pipeline further expands DaaS capabilities by connecting directly to data warehouses, integrating with native applications and user-defined functions (UDF) APIs.
Enhanced Cloud Data Warehouse Interoperability
Cloud data warehouses are becoming central to geospatial data management, forcing a more seamless integration with GIS platforms and ETL tools. Pioneers like Carto have embraced cloud data warehouses as primary geospatial repositories, while traditional GIS tools such as ArcGIS Server and Precisely Spectrum are now integrated with platforms like Snowflake.
Even traditional desktop GIS tools, such as QGIS, MapInfo Pro, and ArcGIS Pro, have started integrating with cloud data warehouses. These tools now allow users to read and query Snowflake and, in some cases, support bidirectional integration, enabling seamless data workflows. This capability marks a significant step forward in bridging the gap between desktop GIS solutions and cloud-based data storage.
Beyond simple connectivity, the geospatial industry is seeing a rise in vendors deploying their software directly within the cloud ecosystem. Companies like Carto and Precisely have begun leveraging native applications to position their solutions closer to the data itself. For geospatial integrators such as Korem, this evolution simplifies the deployment of custom solutions, enabling more efficient workflows through tools like Snowpark containers and User-Defined Function (UDF) integrations.
Raster Processing is Making a Comeback
Raster processing is regaining attention after a few years of people preferring vector data, GeoHash and H3 technologies. The growing demand for imagery to support climate change analysis, insurance risk modeling and 5G propagation are driving new use cases for large-scale raster processing. This often involves combining raster data with other large-scale processing workflows.
Consequently, we are seeing an acceleration of innovations in raster data management and processing:
- Carto has introduced raster processing capabilities with BigQuery and Snowflake, seamlessly integrating with its platform and Carto Workflows to enable automated raster processing.
- Precisely launched the GeoRaster SDK, featuring the advanced Multi-Resolution-Raster (MRR) format for high-performance raster management.
- Wherobots brought Apache Sedona’s distributed raster processing technology to Databricks and Snowflake, expanding the accessibility of scalable raster analysis.
These advancements are pushing the boundaries of what’s possible with raster processing. Industries such as telecommunications and insurance can now leverage large-scale raster datasets with greater efficiency, enabling actionable insights and streamlined workflows. The renewed focus on raster data underscores its critical role in solving modern geospatial challenges.
More Connected and Autonomous Vehicles
The rapid rise of connected and autonomous vehicles is driving the need for advanced high-definition street and navigation data, enriched with near-real-time updates. By 2025, the global number of connected vehicles is projected to reach 400 million, a significant increase from 2021 with 237 million.
Solutions such as HERE HD Live Map and HERE UniMap go beyond standard route and fleet navigation by supporting advanced Driver-Assistance Systems (ADAS), Highly Automated Driving (HAD), and intelligent speed assistance (ISA).
Near-real-time updates flow seamlessly between data providers and the extensive network of connected vehicles. This continuous exchange allows providers to identify and address inaccuracies or gaps in street data. The result is the development of sophisticated datasets on vehicular traffic, which can be applied to numerous use cases.
For example, property and casualty insurers are beginning to use this detailed street and historical traffic data to refine risk assessments and enhance analytics when combined with their telematics data.
Furthermore, the growing number of connected vehicles is improving traffic data accuracy and reliability. This creates opportunities for applications in urban planning, competitive analysis, and evaluating business performance, opening doors for industries like fuel retail and banking.
These trends highlight the constant evolution of geospatial technologies across industries, solidifying a critical dimension of modern problem-solving.
The impact of geospatial technology continues to expand, with innovation driving adoption far beyond traditional GIS audience. While AI, cloud computing, data warehousing and business intelligence often have the spotlight, geospatial intelligence remains an essential foundation for them.
To learn more about those trends, attend the next Enterprise Location Intelligence Summit in Quebec City.
Ready to unlock the potential of these trends for your business? With 30 years of geospatial expertise, Korem can help you stay ahead of the curve.
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