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GeoAI-Driven Intelligent Governance: Leveraging Large Language Models and Geospatial Analytics for Public-Sector Decision Support

  • Sudhanshu Joshi 1,* and 
  • Manu Sharma 2

Received: 14 June 2026 | Revised: 15 September 2026 | Accepted: 08 October 2026 | Published: 03 November 2026

Abstract

Public institutions increasingly rely on diverse geospatial, administrative, environmental, sensor, and citizen-generated data, but turning these assets into trustworthy and usable governance intelligence is challenging. GeoAI integrates and leverages the power of GIS, remote sensing, machine learning and deep learning, spatial analysis, and LLM (large language models) for natural-language interaction and knowledge extraction. This PRISMA 2020-informed systematic review collates 51 studies, covering the development, use, enablers, benefits and barriers to using GeoAI and LLM in public-sector governance. Evidence ranges from urban and regional planning, disaster-risk management, infrastructure, environmental governance, agriculture and natural-resource management, public-service delivery, to monitoring of Sustainable Development Goals. Although the technologies support predictive and conversational decision-making, the literature remains weighted toward technical feasibility, with limited evidence concerning policy effectiveness, service quality, equity, accountability, citizen trust, and long-term public value. Drawing on the Resource-Based View, Dynamic Capability Theory, the Technology–Organization–Environment framework, and Public Value Theory, the study proposes a six-layer GeoAI-Driven Intelligent Governance Framework linking data resources, technological capabilities, analytics, governance functions, decision support, and public-value outcomes. It also develops testable propositions and a future research agenda emphasizing institutional capability, explainability, human oversight, and context-sensitive adoption.

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Joshi, S.; Sharma, M. GeoAI-Driven Intelligent Governance: Leveraging Large Language Models and Geospatial Analytics for Public-Sector Decision Support. Journal of Intelligent Operations and Analytics 2026, 1 (1), 2.
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