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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
The-Vinh Nguyen
Thai Nguyen University of Information and Communication Technology
Computer Science & IT Electrical & Electronics Engineering Engineering

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Multi-agent autonomous GeoAI framework for scalable and self-improving geospatial intelligence Kim-Son Nguyen; The-Vinh Nguyen; Van-Viet Nguyen; Thi-Minh-Hue Luong; Huu-Khanh Nguyen; Duc-Binh Nguyen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2201-2215

Abstract

Large language models (LLMs) have recently expanded the scope of automation across many application domains. In geographic information systems (GIS), however, many tasks still require specialized expertise and remain difficult for non-expert users. Recent studies have explored LLM-based geospatial analysis under a single-agent paradigm, but these early systems remain limited by weak coordination, limited error recovery, and dependence on proprietary artifacts. This study proposes multi-agent autonomous geospatial artificial intelligence (MA-GeoAI), a multi-agent architecture in which the planner, coder, validator, debugger, and knowledge agents collaborate through the LangGraph framework. The framework was evaluated on three case studies: population exposure assessment, mobility pattern analysis, and county-level mortality modeling. Unlike general-purpose multi-agent LLM frameworks, MA-GeoAI embeds spatial semantics, coordinate reference system (CRS) consistency checks, geometry validation, and operation-aware coordination directly into the control loop. Across repeated runs, all evaluated systems completed the controlled artifact contract; therefore, the analysis focuses on auditability, runtime, fallback behavior, and reproducibility rather than binary task-completion superiority.
Toward generalizable unified modeling language automation: a dual case study on class and use case diagram generation Van-Viet Nguyen; Huu-Khanh Nguyen; Kim-Son Nguyen; Thi Minh-Hue Luong; Duc-Quang Vu; The-Vinh Nguyen
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11226

Abstract

Manual generation of unified modeling language (UML) diagrams creates bottlenecks in agile development due to inconsistency and labor intensity. While large language models (LLMs) offer generative capabilities, existing solutions suffer from data scarcity and inadequate evaluation tools. To address this, we present a dual-LLM pipeline integrating lightweight specification generation with reasoning-oriented code synthesis. Uniquely, this framework employs a weighted multimodal validation module utilizing diverse vision-language models (VLMs) to assess diagrammatic fidelity. We further address the data shortage by releasing benchmark datasets comprising 5,000 class and 3,000 use case diagrams. Empirical results demonstrate a 95.8% rendering success rate for class diagrams and strong semantic alignment for use case models. By mitigating structural and behavioral reasoning conflicts, this research provides a replicable architecture and rigorous assessment methodology, establishing a robust foundation for scalable, artificial intelligence-driven automation in software engineering.
Co-Authors Duc-Binh Nguyen Duc-Quang Vu Huu-Khanh Nguyen Huu-Khanh Nguyen Kim-Son Nguyen Thi Minh-Hue Luong Thi-Minh-Hue Luong Van-Viet Nguyen