Summary of Keynote Talk by Peter Rabley, CEO, Open Geospatial Consortium: “Will AI Eat Geospatial?”

With the advent of Generative AI, a question that intrigues the mind of the Geospatial community across the globe is “Will AI eat geospatial?”

With rapid advances in this ‘more than disruptive’ technology that is artificial intelligence, many wonder whether traditional geospatial roles and processes will become obsolete.

The keynote argues that the answer is far more nuanced: AI will not replace geospatial — it will fundamentally reshape it.

The Shift from Map-Making to Spatial Reasoning

Historically, geospatial work has centred on painstaking manual processes: digitising features, annotating maps, cleaning unstructured spatial data, and stitching together diverse datasets. These tasks are increasingly being automated. Modern AI systems can extract features from imagery, classify land use, detect objects, and identify change patterns with unprecedented speed and accuracy.

But the real transformation lies beyond automation. Instead of static maps and layer-based GIS pipelines, AI enables spatial reasoning — the ability to interpret context, integrate diverse data modalities, analyse patterns, and generate dynamic insights. By bringing together satellite imagery, feeds from IoT devices, vector layers, and temporal data, AI systems can answer complex “why”, “what if”, and “what next” questions that were previously hard to answer.

Democratisation Through Natural-Language Interfaces

One of the most significant shifts highlighted in the keynote is the emergence of natural-language and agent-based interfaces. These tools drastically reduce the barrier to entry for geospatial analytics.

Instead of navigating complex GIS software, users — planners, administrators, researchers, citizens — can pose questions in plain language. AI interprets the query, retrieves the relevant geospatial data, performs the analysis, and presents clear insights.

This new trend promises to broaden the impact of geospatial data across sectors such as mobility, climate resilience, agriculture, and governance.

Why Human Expertise Still Matters

Despite automation, the keynote emphasises that human expertise remains indispensable.

AI models are only as reliable as the data they are trained on, and geospatial data is often of poor quality. It is incomplete, noisy, lacks metadata or context-dependent. Experts are therefore needed to:

• curate and validate high-quality spatial datasets,
• interpret AI-generated patterns and anomalies,
• integrate domain knowledge into analyses, and
• ensure responsible and ethical use of spatial insights.

Complex tasks that require an understanding of the underlying policy guidelines, local context, socio-environmental understanding, or judgment cannot be delegated entirely to the AI models.

The Imperative of Governance and Trust

As AI systems generate more sophisticated geospatial outputs, the need for robust data governance, provenance, and transparency becomes even more pressing.
Questions of bias, accuracy, environmental impact, and privacy loom large. The keynote argues for embedding ethical principles and governance frameworks early in the AI-geospatial pipeline — rather than treating them as afterthoughts.

For public institutions, cities, and data-exchange platforms, this emphasis on trust and accountability aligns strongly with the growing need for transparent spatial decision-making.

Implications for Public Good and Urban Systems

For initiatives that leverage geospatial data for societal impact — such as urban mobility planning, climate adaptation, land-use monitoring, or disaster management — AI certainly offers unprecedented opportunities like: faster, scalable spatial analysis; integration of multimodal datasets; real-time situational awareness; and broader access to geospatial intelligence.

However, these gains depend on sustained investment in skilled human oversight, data standards, and responsible governance.

A Future Reimagined, Not Replaced

The keynote concludes with a balanced perspective: AI will not “eat” geospatial, but it will dramatically reshape the discipline.

The value chain will shift from manual digitisation to problem-framing, oversight, and interpretation. Data scientists, GIS professionals, domain experts, and policymakers must collaborate to ensure that AI-driven geospatial systems serve the public good — equitably, transparently, and sustainably.

As we enter this era of AI-augmented geospatial intelligence, the challenge is not to resist the technological shift, but to guide it thoughtfully. The future of geospatial belongs not to machines alone, but to the humans who know how to ask meaningful spatial questions — and ensure that the answers benefit society at large.

For the detailed video please check – https://youtu.be/7uZegDoNWv4