October 2025 Summaries

CARTO and Google Earth AI are collaborating to make planetary-scale insights more accessible by integrating advanced foundation models and Agentic GIS. Google Earth AI, developed by Google Research, utilizes Geospatial Foundation Models (GFMs) to extract insights from diverse data sources like satellite imagery, environmental phenomena, and population dynamics, enabling complex analyses such as risk forecasting and optimal site selection. Through CARTO's cloud-native platform, organizations can leverage these pre-trained models without needing specialized AI or GIS expertise, utilizing low-code workflows and AI Agents to transform data into actionable insights. This integration facilitates faster and more informed decision-making across various sectors, including geomarketing, risk analysis, supply chain optimization, and site selection, by removing traditional barriers to advanced spatial analytics. As a result, organizations can quickly move from data discovery to actionable insights, benefiting from accurate, transparent, and user-friendly geospatial intelligence tools developed by Google and CARTO.

At the Spatial Data Science Conference (#SDSC25) in New York, leaders in data science and analytics explored the transformative potential of Agentic GIS and spatial analytics, emphasizing the shift from traditional GIS to dynamic, conversational intelligence platforms. The event highlighted Agentic GIS's role in democratizing insights and reducing the reliance on specialized teams by enabling business units to self-serve complex queries. Keynote speaker Javier de la Torre from CARTO illustrated the emerging "agentic era" with practical applications like New York's congestion pricing plan, showcasing how integrating Agentic GIS with AI-driven tools can optimize decision-making in sectors such as infrastructure, urban planning, and commercial expansion. The conference also underscored the importance of GeoAI in operational efficiency and strategic growth, and the necessity for professionals to upskill in new technologies to maintain a competitive edge. Industry collaborations with AI giants like Google DeepMind were also noted, highlighting the integration of spatial analytics into large-scale enterprise cloud infrastructures.

The text explores the challenges and opportunities in scaling geospatial AI within organizations, highlighting that only a quarter of AI projects achieve their expected return on investment (ROI). It outlines a five-step roadmap to enhance geospatial AI impact, emphasizing the need for strong data governance, workflow automation, and integration of AI into existing tech stacks. The roadmap encourages organizations to transition from individual productivity tools to enterprise-scale automation, ensuring spatial insights contribute directly to decision-making. The text underscores the importance of enabling all team members, from engineers to decision-makers, to interact effectively with AI systems. It concludes by advocating for a continuous cycle of learning and refinement in AI adoption to achieve organization-wide transformation and measurable impact, with examples from industry leaders like BT and Cotality successfully embedding spatial reasoning into their AI strategies.

Enterprises can now connect AI agents like ChatGPT and Gemini to geospatial data and workflows through the CARTO MCP Server, an innovation aimed at integrating AI with specialized geospatial tools. This integration is facilitated by the Model Context Protocol (MCP), an open standard that allows AI agents to communicate with external tools and data sources, akin to how USB connects devices to computers. This development democratizes access to geospatial insights across organizations, enabling AI agents to perform complex spatial analyses, such as routing optimization and site selection, without requiring GIS expertise. The CARTO MCP Server offers secure, governed access to spatial data within existing cloud environments, maintaining compliance with governance policies. By providing a standardized connectivity layer, MCP eliminates the need for bespoke GIS integrations and allows spatial workflows to be distributed organization-wide. The CARTO platform, with over 200 components, supports advanced predictive analytics and integrates with tools like Google Earth Engine, enhancing the capabilities of AI agents in spatial reasoning and analysis.

GERSification is a process that enhances data integration and analysis by using the Global Entity Reference System (GERS) to link open and premium datasets with common, accessible IDs, thereby eliminating integration bottlenecks. This system enables organizations to connect spatial data sources instantly, accelerating insights and reducing engineering costs, which is particularly valuable in time-sensitive scenarios like disaster response. By using GERS IDs, organizations can enrich data with additional attributes from various vendors, such as business contact details or demographic profiles, resulting in consistent joins and richer insights. The system facilitates a scalable, multi-source data strategy that begins with open data and can effortlessly integrate premium data to create comprehensive views for decision-making. The process is exemplified through scenarios like identifying businesses at risk during an earthquake in San Francisco, where GERSification allows analysts to rapidly access enriched data and take informed actions.

Spatial analytics has evolved significantly in recent years, transitioning from a specialized niche to a crucial business function that influences real-time strategic decisions. The State of Spatial Analytics 2026 report seeks input on cloud adoption, AI integration, skills, and tools to map this evolution. A notable trend is the increased use of cloud platforms for spatial analysis, with 70% of respondents now utilizing cloud solutions, enhancing performance and scalability for large-scale workflows. Despite the growing demand for spatial expertise, there is a persistent skills shortage, with nearly 69% of respondents finding it challenging to hire spatial data scientists. AI adoption in spatial analytics is still limited, with over 25% of organizations not utilizing AI, primarily due to data security concerns and unclear use cases. The report emphasizes the importance of new training strategies to bridge the skills gap and invites contributions to shape the future of spatial analytics.

CARTO has introduced the Site Selection AI Agent, a tool integrated into Google's Gemini Enterprise, designed to simplify the process of selecting new commercial sites by transforming complex geospatial analysis into natural language interactions. This AI-powered tool enables users, regardless of their technical expertise, to make data-driven location decisions for business expansion by performing advanced spatial analyses such as direct site comparisons and market discovery. It leverages authoritative data sources like Google Maps Places Insights and the Google Population Dynamics Foundation Model, providing insights into business ecosystems, demographics, and growth trends. The CARTO Site Selection AI Agent fosters democratized access to geospatial analysis, eliminates manual processes through automation, and offers secure, scalable analytics using Google BigQuery. This innovation is aimed at supporting various industries, including retail, real estate, logistics, and financial services, by offering insights that enhance expansion strategies and optimize site planning.

CARTO's Agentic GIS platform introduces a transformative approach to spatial analysis, utilizing AI Agents to democratize Location Intelligence across organizations. By leveraging these AI Agents, businesses can automate complex workflows, generate instant insights, and maintain security within their cloud environments. This approach allows spatial data to be accessible beyond specialist teams, enabling company-wide decision-making and turning spatial analytics into a driver of business and social value. Companies like TELUS, Cotality, BT, MiFibra, and the Virtue Foundation are already implementing CARTO's platform to enhance decision-making processes, ranging from telecommunications to healthcare. The platform's capabilities include natural language processing, enterprise-grade governance, and interoperability with existing AI ecosystems, ensuring that insights are delivered securely and efficiently.

CARTO has announced its support for the Oracle Autonomous Database, enabling organizations to modernize their geospatial workflows by integrating CARTO's platform with Oracle's robust spatial data capabilities. This collaboration is designed to help companies transition to cloud-native spatial analytics, leveraging AI and automation within the Agentic GIS era. Oracle, known for its comprehensive geospatial support, provides advanced functionalities such as vector geometries, raster data, and network data models. With CARTO's integration, users can manage and analyze data using native Oracle connectivity, as well as visualize extensive datasets through interactive maps and dashboards. The partnership allows for modern spatial workflows that accommodate real-time operations, AI-driven analytics, and collaborative, low-code tools, all while preserving the value of existing Oracle investments. This integration not only enhances traditional workflows but also unlocks new use cases like advanced routing, linear referencing, raster analytics, and 3D point cloud processing. CARTO's platform is compatible with Oracle Database 23c and 19c and can be deployed across various cloud environments, offering flexibility and scalability to meet specific organizational needs.

CARTO's Q3 2025 updates focus on enhancing geospatial data analysis and decision-making through the cloud with a series of improvements designed to make map-building more flexible, workflows smarter, and data governance more transparent. Key features include the ability to link widgets to raster sources for dynamic insights, add non-visible data sources for focused visualizations, and utilize new control components like Conditional Split and Success/Error Split for adaptive workflows. The Territory Planning Extension introduces tools for optimizing territory balancing and facility placement, while enhancements to data import processes make handling large geospatial files more efficient. Governance improvements include SQL audit trails for query transparency and new settings for admins to enforce consistent policies, ensuring secure and compliant spatial deployments. These updates aim to provide more powerful and resilient spatial analysis capabilities, supporting organizations in making data-driven decisions with precision and efficiency.

CARTO's Territory Planning Extension is designed to enhance decision-making by enabling smarter location strategies for territory balancing and logistics optimization, utilizing advanced tools for spatial analysis. This extension integrates with CARTO's low-code platform, facilitating the design and automation of spatial strategies through features like Territory Balancing, which divides areas into optimized territories based on metrics like consumer demand, and Location Allocation, which identifies optimal facility locations to minimize costs or maximize coverage. These capabilities are applied in various scenarios, such as retail expansion, sales territory design, healthcare access planning, and last-mile logistics, allowing organizations to make data-driven decisions to improve efficiency, growth, and fairness. The extension supports the transformation of spatial data into actionable strategies, addressing challenges from workload distribution to network management, and is accessible through a free trial for users interested in exploring its potential.