Reasoning Is All You Need for Urban Planning AI
Sijie Yang, Jiatong Li, Filip Biljecki

TL;DR
This paper proposes a reasoning-based AI framework for urban planning that enhances decision-making transparency, compliance, and stakeholder deliberation by integrating cognitive layers and logic components, advancing beyond statistical learning methods.
Contribution
It introduces the Agentic Urban Planning AI Framework, combining reasoning capabilities with multi-agent collaboration to improve urban planning decisions and transparency.
Findings
Framework integrates perception, foundation, reasoning layers.
Logic components enable explicit analysis, verification, and decision-making.
Benchmark metrics for evaluating reasoning agents are proposed.
Abstract
AI has proven highly successful at urban planning analysis -- learning patterns from data to predict future conditions. The next frontier is AI-assisted decision-making: agents that recommend sites, allocate resources, and evaluate trade-offs while reasoning transparently about constraints and stakeholder values. Recent breakthroughs in reasoning AI -- CoT prompting, ReAct, and multi-agent collaboration frameworks -- now make this vision achievable. This position paper presents the Agentic Urban Planning AI Framework for reasoning-capable planning agents that integrates three cognitive layers (Perception, Foundation, Reasoning) with six logic components (Analysis, Generation, Verification, Evaluation, Collaboration, Decision) through a multi-agents collaboration framework. We demonstrate why planning decisions require explicit reasoning capabilities that are value-based (applying…
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Taxonomy
TopicsGeographic Information Systems Studies · Land Use and Ecosystem Services · Constraint Satisfaction and Optimization
