We are excited to announce our latest vision paper (preprint), “GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS”, a collaborative effort involving 16 leading GIScience and computer science scholars across academia, national labs, and government agencies, presents a timely research agenda for the next-generation AI-powered GIS—one that is autonomous, intelligent, and more accessible.
Over the past two years, we have been developing foundational ideas and prototype systems that explore how large language models (LLMs) can serve as decision-making cores and build geoprocessing workflows in GIS applications. This vision paper is built on our earlier work, particularly the 2023 paper that formally introduced and defined the concept of Autonomous GIS (Li & Ning, 2023), and demonstrates early-stage implementations, and outlines a roadmap for advancing this emerging paradigm.
To conceptualize how Autonomous GIS works, we propose a framework that includes five core autonomous goals: self-generating, self-executing, self-verifying, self-organizing, and self-growing. These goals emphasize the system’s capacity to initiate geospatial inquiries, carry out data-driven tasks, evaluate its own performance, manage limited resources, and learn from both successes and failures.We also introduce a structured framework of five autonomous levels: Level 1: Routine-aware GIS, which automates predefined processes; Level 2: Workflow-aware GIS, capable of generating and executing workflows based on user input; Level 3: Data-aware GIS, which can autonomously identify, retrieve, and prepare appropriate datasets; Level 4: Result-aware GIS, which can evaluate its outputs and iteratively refine its approach; and Level 5: Knowledge-aware GIS, a fully autonomous system that learns from experience and external knowledge to improve over time. Most of the current research is focused on Level 2 agents—those that can autonomously generate and implement spatial workflows based on natural language input. Progressing to Level 3 and beyond will require substantial advancements in data reasoning, uncertainty management, and adaptive learning.
In addition to outlining theoretical principles, the paper highlights several proof-of-concept implementations developed by our lab to demonstrate how AI can automate different components of the spatial analytical process. These include LLM-Find, a natural language-driven data retrieval agent; LLM-Geo, a workflow-generation agent for spatial analysis; LLM-Cat, a vision-enabled agent for autonomous cartography; and GIS Copilot, a QGIS plugin that assists users in conducting terrain analysis with existing tools.
While this goal remains aspirational, the foundational work has already begun. We invite geospatial and AI researchers, developers, and educators to collaborate in shaping this future, where GIS becomes not only more powerful and scalable, but also to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.
You can access the preprint here and the original Autonomous GIS definition paper here.
Evolution of GIS driven by major disruptive technologies.
Conceptual framework of autonomous GIS
Levels of autonomous GIS, inspired by Mike Lemanski (Smith, 2016)
