GISCafe Geospatial Predictions for 2025 – KOREM predictions

By Jonathan Houde, CTO at Korem

Jonathan Houde

The geospatial industry, similar to the broader technology sector in general, has experienced another remarkable year of innovation. Geospatial solutions are continuously evolving to address complex challenges and unlock new opportunities for businesses. Many of 2024’s predictions have started to materialize, with advancements spanning GeoAI, cloud data warehousing, expanded data availability, and more.

While it’s challenging to narrow down such a wide range of innovation, this article will focus on 5 key trends shaping the future of location intelligence in 2025.

Generative AI Expanding into Geospatial Applications

Generative AI (GenAI) has continued to generate buzz, evolving from hype to tangible use cases on geospatial applications. From generating code to analyzing and summarizing data, GenAI’s outcome represents a significant productivity boost.

Emerging tools like conversational GIS allows users to interact with maps and data through natural language. Many software companies are now embedding generative AI into their platforms. Carto’s AI-powered assistant is a great example, making it easier than ever to extract deeper insights from analysis.

In the business world, GenAI is transitioning from the experimental phase to proof-of-concept (POC) implementation, but challenges persist. According to experts, roughly 80% of AI projects fail to reach production, often due to insufficient ROI, poor change management, or shadow AI operating outside governance frameworks. However, the most common problem appears to be related to data integrity, meaning that data is not AI-ready due to a lack of quality and context.

For instance, companies leveraging GeoAI for satellite imagery feature extraction depend on precise geocoding to prevent costly operational errors. By enriching AI models with geospatial context, businesses can achieve more reliable outputs. Techniques such as retrieval-augmented generation (RAG), dynamic prompt generation, and session-based context supplementation can integrate location intelligence into AI workflows, enhancing their effectiveness and resilience.

Data as a Service (DaaS) Gaining Momentum

The data offering has evolved significantly, driven by the rise of Data as a Service (DaaS) solutions. From broad marketplace offerings to enhanced interoperability with geospatial standards like GeoIceberg and GeoParquet, DaaS is enabling improved data sharing and greater access to open data from government and open-source initiatives.

API-based DaaS solutions are leading the way, allowing businesses to consume on-demand data such as imagery and dynamic feature extraction, as seen with Vexcel Data’s Property Attributes.

To meet the growing demand for data enrichment, Precisely’s Data Graph API enables the retrieval of unlimited attributes from hundreds of datasets in a single, high-performance API call. Precisely’s Data Integrity Suite Pipeline further expands DaaS capabilities by connecting directly to data warehouses, integrating with Snowflake native applications and user-defined functions (UDF) APIs. APIs.

These innovations demonstrate how DaaS is transforming access to enriched, dynamic, and interoperable data, empowering businesses to make smarter, faster decisions.

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 desktop GIS tools like QGIS and MapInfo Pro support cloud data warehouse connectivity. Additionally, solutions like Snowflake native applications and Snowpark containers enable the deployment of custom geospatial solutions. Centralizing data at a specific location enables both geospatial and AI capabilities, allowing businesses to accelerate their analytics, and ultimately their innovation.

Raster Processing is Becoming Cool Again

Raster processing is experiencing a resurgence after a few years of preference for vector data, GeoHash and H3 technologies.

With the growing demand and availability of aerial imagery and cost-effective satellite to support climate change analysis, insurance risk assessment 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.

Innovations in raster data management and processing, such as Carto’s tools for Snowflake and BigQuery, Apache Sedona, or Precisely’s GeoRaster SDK and their advanced Multi-Resolution-Raster (MRR) format, are increasing the reach and scale of raster processing.

These advancements simplify the analysis and automation of workflows for large-scale raster datasets, providing actionable insights for industries such as telecommunications and insurance.

Increased in Connected and Autonomous Vehicles

The proliferation of connected and autonomous vehicles is fueling demand for advanced high-definition street and navigation data, supported by near-real-time updates. The number of connected vehicles worldwide is expected to reach 400 million by 2025, compared to 237 million in 2021.

Platforms like HERE HD Live Map and HERE UniMap, not only provide standard Route and Fleet Navigation, but also enhance advanced Driver-Assistance Systems (ADAS), Highly Automated Driving (HAD) solutions and intelligent speed assistance (ISA).

Near-real-time updates flow from data providers to a vast network of connected vehicles, while insights are sent back to providers to identify inaccurate or missing street data. This feedback enables the creation of advanced datasets on vehicular traffic, which can be used to support a variety of use cases.

For instance, P&C insurers are starting to leverage this high-resolution street data and historical traffic data to improve risk ratings and improve analytics with their own telematic data.

Additionally, the growing number of connected vehicles is improving the reliability of traffic data, enabling a wide range of use cases, such as urban planning, competitor analysis, and business performance evaluation. This opens up new opportunities for fuel retailers and bank branches.

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These trends don’t cover the entire geospatial landscape, but they highlight the evolution of geospatial technologies and their impact on decision-making across industries.

In 2025, we anticipate that artificial intelligence will remain a buzzword, while location intelligence will remain in the background, but as crucial. Topics like AI, cloud computing, data warehousing, and business intelligence often have the spotlight, yet geospatial technologies remain an essential foundation for these. Learn more at korem.com.

Jonathan Houde, CTO at Korem, jhoude@korem.com

Jonathan is responsible for technical leadership and innovation, further developing the company’s technical community, and aligning its software strategy, architecture and partner relationships to solve business challenges and deliver the best customer value. With his extensive experience across many different technologies, Jonathan and his team are able to build technology solutions for complex projects across different industries. He is known for his ability to quickly identify customer requirements and translate them into recommendations and

Tags: AI integration, data visualization, Geospatial, GIS trends, Location Intelligence, predictive analytics

Category: Industry Predictions

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