City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification
Rui Liu, Steven Jige Quan, Zhong-Ren Peng, Zijun Yao, Han Wang, Zhengzhang Chen, Kunpeng Liu, Yanjie Fu, Dongjie Wang

TL;DR
This paper introduces a hierarchical agentic framework for efficient, robust, and consistent urban plan modifications using structured geospatial data and natural language instructions, improving urban renewal processes.
Contribution
It presents a novel hierarchical agentic system that decomposes natural language edits into geometric intents and coordinates multi-level urban plan modifications.
Findings
Significant efficiency improvements in urban plan editing.
Enhanced robustness and correctness in modifications.
Maintained spatial validity and consistency during edits.
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…
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Taxonomy
TopicsGeographic Information Systems Studies · 3D Modeling in Geospatial Applications · Constraint Satisfaction and Optimization
