City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification

Abstract:Urban renewal requires incremental modifications to existing geospatial plans, yet manually updating complex layouts under spatial constraints is labor-intensive and error-prone. To tackle this, we propose CEAE, a hierarchical agentic framework that formulates urban renewal as machine-executable GeoJSON editing from natural-language instructions. CEAE decomposes instructions into hierarchical geometric intents, executing edits from coarse to fine while preserving spatial consistency through a self-reflective execution-validation loop. Experimental results show that CEAE outperforms baselines in execution validity, robustness, and geometric accuracy.

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)

arXiv:2602.19326 [cs.MA]

(or arXiv:2602.19326v3 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2602.19326 Focus to learn more arXiv-issued DOI via DataCite

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