September 2026 Summaries

Portolan is presented as an open, cloud-native approach to spatial data infrastructure designed to improve how authoritative geospatial data is published, discovered, queried, and governed. It responds to limitations of traditional portals and API-based services by enabling publishers to store data in open formats such as GeoParquet, Cloud-Optimized GeoTIFF, PMTiles, and potentially Apache Iceberg, allowing users and applications to query files directly from publisher-controlled object storage. The approach combines standardized STAC catalogs, human-readable documentation, agent-oriented guidance, validation tools, and a registry of independently hosted catalogs to support federation without centralizing datasets. The text argues that AI agents can make these distributed data sources more accessible by discovering relevant datasets, performing reproducible spatial analyses, and returning sourced results, while open formats and separable choices of storage, compute, AI models, and semantics support data sovereignty. CARTO SDI is described as a commercial implementation built around the open Portolan catalog, adding catalog interfaces, natural-language AI access, governance, permissions, telemetry, quality controls, monetization, and support for legacy GIS services while leaving data in the publisher’s chosen storage environment. The initiative remains early, with approximately 20 registered catalogs, and seeks broader adoption, format support, client integrations, and independent implementations.

CARTO’s PlacePulse Embeddings, developed with Applied Geographic Solutions, represent locations across the United States as 256-dimensional vectors that combine demographic, commercial, and environmental characteristics into searchable digital fingerprints. The approach is presented as a way for retailers and other location-based organizations to identify what their highest-performing sites have in common without manually selecting and weighting hundreds of individual variables. By using cosine-similarity searches on embeddings for top-performing store catchment areas, organizations can create a “retail fingerprint,” map areas with similar profiles, rank expansion opportunities from Prime to Low Potential, and incorporate constraints such as distance from existing stores to assess infill or new-market opportunities. Available through CARTO’s Data Observatory and Workflows, the reusable embeddings can support broader applications including site selection, market segmentation, prediction, and identifying promising locations for businesses such as banks, clinics, dealerships, and gyms.