A policy proposal has emerged suggesting that Chungbuk’s urban planning should move away from static document- and drawing-centric ex-post reviews, transitioning instead toward evidence-based diagnosis, alternative comparison, plan consistency review, and ex-post monitoring utilizing AI and spatial data.

Dr. Choi Jung-hyun, a senior research fellow at the Chungbuk Research Institute (CRI, President Kim Young-bae), diagnosed the current status and limitations of Chungbuk Province’s urban planning system through a research project titled "Chungbuk Province Urban Policy Great Transformation: AI Augmented Urban Planning," and presented a direction for introducing an AI-based urban planning support system tailored to regional conditions.

According to the study, setting all 11 cities and counties across Chungbuk Province as the spatial scope, Cheongju-si, Chungju-si, and Okcheon-gun were analyzed as representative cases for large city, medium-small city, and rural types, respectively, considering the characteristics of each urban type. The diagnostic results identified major limitations, including ▲insufficient data connectivity among planning hierarchies, ▲static reviews centered on drawings and text records, ▲lack of data-based scenario comparisons, and ▲gaps in AI and data utilization capacity among cities and counties.

In response, Dr. Choi proposed an AI-based urban planning support system for Chungbuk Province structured in a three-layer model: “Data Layer – Analysis & AI Layer – Planning Support Layer.” This approach standardizes urban planning data, analyzes current urban status and plans using AI, and utilizes the results for plan formulation, administrative review, and monitoring.

Notably, the study defined AI not as a means to automate urban planning decisions, but as “AI-Augmented Urban Planning” to assist the judgment of planners and administrative agencies. By urban type, it proposed selectively applying functional modules across all 11 cities and counties, such as Cheongju (comprehensive diagnosis and planning support model for large cities), Chungju (living zone reorganization and old downtown management model), and Okcheon (rural living zone maintenance and living service accessibility model).

Dr. Choi Jung-hyun stated, “AI should not be used as a technology that makes urban planning decisions on our behalf, but rather as a tool to support the judgment of planners and administrative agencies,” adding, “We must build a Chungbuk-style AI urban planning support system by establishing a foundation for urban planning data and progressively pursuing pilot projects tailored to each urban type.”

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