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[2602.19326] City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification
Summary
The paper presents a hierarchical framework for urban geospatial modification, enabling efficient urban renewal through agentic systems and multimodal reasoning.
Why It Matters
As urban areas face increasing challenges like congestion and functional imbalance, this research offers a novel approach to urban planning that enhances efficiency and accuracy in modifying existing layouts. The proposed method could significantly streamline urban renewal processes, making it relevant for city planners and developers.
Key Takeaways
- Introduces a hierarchical agentic framework for urban editing.
- Utilizes GeoJSON for structured representation of urban layouts.
- Implements iterative execution-validation to maintain spatial consistency.
- Demonstrates improvements in efficiency and correctness over existing methods.
- Addresses the need for adaptive urban planning in evolving cities.
Computer Science > Multiagent Systems arXiv:2602.19326 (cs) [Submitted on 22 Feb 2026] Title:City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification Authors:Rui Liu, Steven Jige Quan, Zhong-Ren Peng, Zijun Yao, Han Wang, Zhengzhang Chen, Kunpeng Liu, Yanjie Fu, Dongjie Wang View a PDF of the paper titled City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification, by Rui Liu and 8 other authors View PDF HTML (experimental) Abstract:As cities evolve over time, challenges such as traffic congestion and functional imbalance increasingly necessitate urban renewal through efficient modification of existing plans, rather than complete re-planning. In practice, even minor urban changes require substantial manual effort to redraw geospatial layouts, slowing the iterative planning and decision-making procedure. Motivated by recent advances in agentic systems and multimodal reasoning, we formulate urban renewal as a machine-executable task that iteratively modifies existing urban plans represented in structured geospatial formats. More specifically, we represent urban layouts using GeoJSON and decompose natural-language editing instructions into hierarchical geometric intents spanning polygon-, line-, and point-level operations. To coordinate interdependent edits across spatial elements and abstraction levels, we propose a hierarchical agentic framework that jointly performs multi-level planning and executio...
