Autonomous GIS uses large language models as its reasoning core to automate spatial data collection, analysis, modeling, and visualization for solving spatial problems with minimal human intervention. Often implemented through autonomous geospatial agents that work independently or collaboratively, it represents a shift from GIS as a passive software environment requiring expert operation towards systems capable of task-oriented interaction, workflow generation and increasingly autonomous spatial analysis and decision support. However, current GIS agents are often isolated and tightly coupled with specific platforms, tools, or applications, which limits their reuse and interoperability across systems and domains. In this paper, we introduce Geospatial Agentic Services, or GAS, as a service-oriented framework for publishing, discovering, invoking, and composing GIS agents as interoperable geospatial services. GAS extends traditional geospatial interoperability beyond access to data and predefined operations towards geospatial intelligence interoperability, where GIS agents function as discoverable, reusable, and collaborative service entities that encapsulate spatial knowledge and reasoning. Building on service-oriented architecture, GAS supports structured geospatial skill declarations, agentic service registry and discovery, and distributed agent orchestration. It also integrates validation, provenance, reproducibility, and governance to support transparent and trustworthy geospatial analyses. To demonstrate the framework, we present a GAS implementation and illustrate its use through common invocation modes and representative AI-orchestrated workflows. By connecting traditional geospatial service architecture with emerging agentic interoperability, GAS provides a conceptual and architectural foundation for interoperable, reusable, and scalable agentic geospatial services and workflows in the era of autonomous GIS.

正在加载阅读记录…