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 execution with explicit propagation of intermediate spatial constraints. We further introduce an iterative execution-validation mechanism that mitigates error accumulation and enforces global spatial consistency during multi-step editing. Extensive experiments across diverse urban editing scenarios demonstrate significant improvements in efficiency, robustness, correctness, and spatial validity over existing baselines.

翻译：随着城市随时间演进，交通拥堵与功能失衡等挑战日益凸显，亟需通过对现有规划进行高效修改而非完全重新规划来实现城市更新。实践中，即使微小的城市改动也需要大量人工工作来重绘地理空间布局，这延缓了迭代规划与决策流程。受智能体系统与多模态推理最新进展的启发，我们将城市更新形式化为一种机器可执行任务，该任务可迭代修改以结构化地理空间格式表示的现有城市规划。具体而言，我们采用GeoJSON表示城市布局，并将自然语言编辑指令分解为跨越面要素、线要素与点要素操作的层次化几何意图。为协调跨空间要素与抽象层次的相互依赖编辑，我们提出一种层次化智能体框架，该框架通过显式传播中间空间约束，联合执行多层次规划与执行。我们进一步引入迭代执行-验证机制，以减轻多步骤编辑过程中的误差累积并确保全局空间一致性。在多样化城市编辑场景中的大量实验表明，本方法在效率、鲁棒性、正确性与空间有效性方面较现有基线均有显著提升。