MMUEChange: A Generalized LLM Agent Framework for Intelligent Multi-Modal Urban Environment Change Analysis

• URL: http://arxiv.org/abs/2601.05483v1
• Date: Fri, 09 Jan 2026 02:34:35 GMT
• Title: MMUEChange: A Generalized LLM Agent Framework for Intelligent Multi-Modal Urban Environment Change Analysis
• Authors: Zixuan Xiao, Jun Ma, Siwei Zhang,
• Abstract summary: MMUEChange is a multi-modal agent framework that flexibly integrates heterogeneous urban data.<n>Case studies include a shift toward small, community-focused parks in New York, and the spread of concentrated water pollution across districts in Hong Kong.
• Score: 7.396133065771444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding urban environment change is essential for sustainable development. However, current approaches, particularly remote sensing change detection, often rely on rigid, single-modal analysis. To overcome these limitations, we propose MMUEChange, a multi-modal agent framework that flexibly integrates heterogeneous urban data via a modular toolkit and a core module, Modality Controller for cross- and intra-modal alignment, enabling robust analysis of complex urban change scenarios. Case studies include: a shift toward small, community-focused parks in New York, reflecting local green space efforts; the spread of concentrated water pollution across districts in Hong Kong, pointing to coordinated water management; and a notable decline in open dumpsites in Shenzhen, with contrasting links between nighttime economic activity and waste types, indicating differing urban pressures behind domestic and construction waste. Compared to the best-performing baseline, the MMUEChange agent achieves a 46.7% improvement in task success rate and effectively mitigates hallucination, demonstrating its capacity to support complex urban change analysis tasks with real-world policy implications.

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