Unlocking Your GIS Data's Potential Through AI-Driven Queries

As spatial data continues to grow in both volume and strategic importance, the demand for more intelligent, efficient, and accessible ways to analyze and apply that data is increasing. For many GIS professionals and non-GIS users, the ability to quickly extract meaningful insights from complex datasets plays a critical role in planning, service delivery, and long-term asset management.

Recent advancements in Artificial Intelligence (AI), particularly in natural language processing and spatial query automation, are helping to streamline workflows, reduce technical barriers, and extend analytical capabilities across teams. Rather than replacing domain expertise, AI has the potential to support and strengthen it, enabling users to engage with data more effectively and make more informed decisions.

Rethinking the Role of AI in Spatial Workflows

Traditionally, generating spatial queries required deep technical knowledge: writing SQL, understanding spatial data types like SDO_GEOMETRY, and applying spatial functions such as SDO_WITHIN_DISTANCE or SDO_INTERSECTION. These tools are essential, but often inaccessible to those without specialized training. AI introduces an alternative. By interpreting natural language inputs and converting them into spatially aware queries, AI allows a broader range of users to directly engage with geospatial data.

This shift does not diminish the role of technical specialists. Instead, it reduces time spent on repetitive tasks, those that can be automated without diminishing value, allowing skilled professionals to focus on more complex, strategic analysis. The result is a more efficient use of expertise across the team.

In addition, it allows domain specialists in their own field such as design engineers and asset managers to intuitively query their data in a simple interface enhancing productivity and self-help while improving insight in their own data.

Turning Complexity into Clarity

The integration of AI into spatial platforms helps make advanced spatial operations more approachable. Technologies such as Oracle’s 23ai, which is the most advanced spatial database, introduces in-database machine learning, vector search, and semantic search, accelerating analysis and enabling contextual querying across large datasets. These capabilities are designed to support classification, relationship analysis, and spatial pattern detection with greater speed and scale.

In practice, this means AI allows users to ask natural language questions, such as “Show me all parcels that are crossed by a waterpipe,” or “Find all assets within 50m of a main road,” and receive meaningful results without needing to construct detailed SQL queries. Within Open Spatial’s Enlighten platform, these prompts will be interpreted and executed automatically, with the system selecting relevant records using Oracle Spatial functions. This improves accessibility for planners, engineers, and asset managers; reduces dependency on technical specialists; and supports consistent, reliable spatial analysis.

Preview of Enlighten's Natural Language AI Search feature.

Staying Grounded in Reality

While AI offers valuable capabilities, it is not without limitations. In some cases, AI-generated outputs may appear accurate but fail to meet technical or contextual standards. This challenge, sometimes referred to as “Artificial Illusion” or hallucination (as opposed to “Artificial Intelligence”), underscores the need for appropriate safeguards.

One way to enhance data security is not to expose your data to the AI model but only to share the data structure. With this, the AI can generate the necessary queries to run on your dataset without having access to the data itself reducing data security risks.

Successful implementation depends on governance frameworks that preserve data quality, enable traceability, and support user oversight. The ability to review, edit, and validate AI-assisted outputs is essential for maintaining analytical integrity. Furthermore, effectively using AI to support spatial analysis requires the continued development of user skills, particularly in crafting clear natural language queries and interpreting spatial results within an operational context.

Integrating with Purpose, Delivering with Balance

As Artificial Intelligence becomes more integrated into GIS technologies, its adoption must be approached with both ambition and caution. The ability to automate complex spatial queries and lower the barrier to data access is significant, but effectiveness depends on how these tools are implemented and governed.

AI models are capable of interpreting natural language and generating spatial queries, but they must be deployed in a way that promises clarity, accuracy, and accountability. Users need the ability to understand how a result was generated, verify its validity, and refine it as needed. Transparency in query construction is essential, not only for accuracy, but for maintaining confidence in the outcomes.

Equally important is the need for security and access control. AI-enhanced spatial tools must operate within existing data governance structures. Role-based permissions, data sensitivity classifications, and audit trails should continue to apply to AI-driven outputs in the same way they do to traditional query methods. At Open Spatial we configured an innovative solution to generate the queries without exposing internal data, by isolating the data structure, as mentioned previously. This allows data security whilst realizing the benefits of AI.

The most effective use of AI in GIS is not indiscriminate automation, but targeted implementation; extending the capabilities of users, improving access to information, and streamlining workflows, all without compromising on data integrity or oversight. As this technology evolves, a measured standards-aligned approach will remain critical to ensuring its value over time.

A Smarter, More Accessible GIS

The benefits of AI-driven query assistance are already becoming evident in GIS environments. Organizations are beginning to see improved:

• Usability – Enabling non-technical users to generate complex queries through natural language

• Efficiency – Accelerating spatial workflows and reducing turnaround time

• Consistency – Standardizing query logic and reducing the likelihood of manual errors

• Scalability – Supporting analysis across large, complex datasets with optimized performance

• Governance – Maintaining transparency, validation, and compliance with established data protocols

AI should not be viewed as a disruption to spatial disciplines, but as potential opportunities. By reducing the overhead required to access and analyze geospatial information, AI enables GIS professionals to focus on strategic outcomes: understanding spatial patterns, anticipating risks, and making data-driven decisions with greater confidence.

As AI continues to mature, its role in spatial systems will likely expand. The key will be to adopt these tools with intention, ensuring they support, rather than replace, the domain knowledge that remains central to effective geospatial decision-making.

Join the Discussion

Interested in exploring how AI-driven queries are shaping the future of GIS? Join us for our upcoming webinar, “Unlocking your GIS Data’s Potential Through AI-Driven Queries,” happening on Wednesday, August 13, 2025 at 10:00AM AET or 2:00PM PDT. During this webinar we’ll dive deeper into real-world applications, demonstrate query workflows, and answer your questions live. Register today.