May 2026 Summaries

The efforts to integrate geospatial data as a first-class citizen within the open data ecosystem have seen significant progress, particularly through the implementation of GeoParquet and Apache Iceberg V3. These standards aim to make geospatial data open, governed, and queryable across various engines, optimizing metadata rather than relying on specialized spatial infrastructure. A public testbed was created to evaluate the extent to which this promise has been realized across different data engines. Snowflake emerged as a leader, successfully implementing Iceberg V3's native geometry and spatial predicates, while DuckDB is nearing full support. Other engines, such as BigQuery and Databricks, face challenges mainly related to supporting the new geometry types rather than the Iceberg V3 format itself. The ongoing advancements signify a shift towards native geometry support, with Snowflake showcasing a complete implementation and other platforms expected to follow suit. This development is crucial for removing barriers between GIS and modern cloud analytics, ensuring geospatial data can be seamlessly integrated into existing analytical workflows.

In 2026, AI Agents have revolutionized the field of GIS by enabling the creation of production-ready analytical maps through platforms like CARTO, significantly streamlining the process of map visualization. These AI-driven tools, such as Claude Code and Copilot, can now compose and ship interactive maps from a simple prompt, performing tasks that previously required complex software navigation by GIS professionals. This advancement allows for rapid prototyping and routine tasks, freeing up GIS teams to focus on more nuanced cartographic work. CARTO for Agents is specifically designed to integrate with GIS teams, facilitating the creation of maps from natural language instructions and ensuring that the resulting maps maintain the same governance and security as those created traditionally. The introduction of CARTO MCP Server and its associated apps further enhances the user experience by allowing interactive maps to be rendered directly within conversational interfaces, providing both temporary visual checks and shareable, governed assets. This new approach not only speeds up the map-making process but also seamlessly integrates with existing data governance structures, enabling faster decision-making and collaboration across organizations.

For GIS analysts and specialists considering a transition from Esri to CARTO, preparation is key to a smooth migration, with most of the groundwork needing to be done before moving any data. The process involves understanding the current inventory of GIS assets, determining where business data resides, calculating the total cost of ownership, identifying key personnel for data governance, and defining critical use cases aligned with AI and cloud strategies. Successful migration hinges on involving the right stakeholders early and integrating spatial data with existing business data to enhance analytics capabilities. The Esri migration readiness assessment can help tailor a personalized plan by evaluating these aspects, ensuring a straightforward transition and setting the foundation for future spatial analytics expansion.

In the realm of logistics and fleet intelligence, Snowflake’s OpenRouteService (ORS) Native App integrates routing capabilities directly within the Snowflake environment, eliminating the need for external API calls and maintaining data governance and security. This self-contained routing engine, available via Snowpark Container Services, provides SQL-callable functions for directions, isochrones, matrix, and optimization tasks, traditionally managed by third-party location data services. The CARTO Workflows extension package enhances these functionalities by transforming each SQL function into a drag-and-drop component for analytical pipelines, facilitating tasks like generating isochrones, computing distance matrices, and optimizing vehicle routes. Moreover, these workflows can be published as MCP tools for AI Agents, enabling automated geospatial analyses within CARTO Builder and Snowflake platforms. The integration supports AI-driven decision-making in fleet management, where an AI Agent can autonomously execute routing tasks and present results through interactive maps and charts. This innovation positions CARTO as a leader in agentic geospatial intelligence, offering seamless compatibility with Snowflake Intelligence and Cortex Code, thus reinforcing the potential for cloud-native spatial analytics.

At the 2026 Spatial Data Science Conference held at the Royal Geographical Society in London, key industry themes emerged, highlighting the integration of AI into geospatial workflows, making spatial intelligence more conversational and automated. A significant announcement was CARTO for Agents, which allows GIS professionals to work seamlessly with AI agents using conversational workflows and a CLI-friendly interface, enabling the creation of maps, analysis, and application development without traditional interfaces. The conference underscored the move towards open infrastructure, with geospatial data increasingly integrated into cloud-native, interoperable platforms, enhancing accessibility and functionality. Sessions also explored how geospatial AI is making environmental risks financially visible and how urban analytics are transforming cities into adaptive systems for real-time management. Throughout the event, there was an emphasis on the human element in geospatial work, recognizing that technology serves as a tool to enhance human expertise and judgment, indicating a shift in how spatial systems are designed and implemented within organizations.

CARTO is transforming the geospatial industry by integrating AI agents into its platform, enabling a seamless collaboration between humans and AI in spatial analytics. By transitioning from traditional GIS interfaces to command-line interfaces (CLI), APIs, and machine communication protocols (MCP), CARTO allows AI agents to manage tasks such as connecting to data warehouses, conducting spatial analysis, and publishing maps without a graphical user interface. The platform's new features, including the CARTO CLI, Agent Skills, and MCP Server, facilitate the automation of complex geospatial workflows, ensuring traceability and governance. These advancements make CARTO compatible with major agentic platforms like OpenAI Codex, Microsoft Copilot, and more, allowing organizations to integrate AI-driven geospatial analysis into their existing systems. This shift aims to keep pace with the fast-evolving tech landscape, offering an innovative approach to geospatial data handling and visualization.

Satellite imagery has traditionally been a challenging yet invaluable resource for geospatial intelligence due to its complexity and the manual labor required for analysis. However, the integration of AI geospatial foundation models is revolutionizing this field, as demonstrated by the collaboration between CARTO and LGND AI. This partnership introduces the LGND Embeddings API into CARTO Workflows, allowing users to process and analyze satellite imagery without coding or a background in machine learning. By converting satellite images into vector embeddings that capture the visual essence of specific areas, analysts can efficiently search, analyze, and detect changes on the planet’s surface. This technology transforms workflows across various industries such as insurance, real estate, and telecommunications by enabling automated processes like post-disaster claims triage, identifying undeveloped land, and prioritizing broadband rollout. With tools like change detection and visual similarity search, organizations can leverage satellite imagery for strategic decision-making in a scalable and timely manner.

CARTO AI Agents are designed to streamline repetitive geospatial analysis tasks for GIS teams by allowing stakeholders to directly interact with a conversational AI interface to obtain spatial insights. This automation enables business users to self-serve routine requests, such as demographics analysis around candidate sites, location comparisons, market sizing, and territory planning, without needing GIS expertise or waiting for a GIS team. By utilizing CARTO's Agent Configuration Assistant, organizations can easily build AI Agents that handle these tasks, freeing GIS professionals to focus on more complex, high-value problems. These agents use predefined workflows and data sources to deliver consistent, on-demand results, effectively turning routine GIS tasks into an automated process that serves a broader audience while enhancing the GIS team's strategic role within the organization.