Multi-Source Data-Driven Brownfield Identification: Methodology and Spatial Characteristics of Third Line Construction Cities

Ding Ding; Huairu Zhang; Yi Cao; Di Wang; Sijia Sun

doi:10.53941/ubs.2026.100011

Abstract

Global practices in brownfield redevelopment have yielded valuable legislative and policy frameworks. However, existing studies lack targeted identification and database construction methodologies for Third Line Construction cities in China, which feature clustered industrial heritage and fragmented land rights, leading to a disconnect between general brownfield research and the actual governance needs of such typical industrial cities. This study addresses these gaps by developing a multi-source data integrated technical framework for systematic brownfield identification and standardized database construction, taking Mianyang—a typical Third Line Construction city—as the case. The framework prioritizes planning data, takes Point of Interest (POI) data as the core supplement, and innovatively integrates satellite imagery and industrial statistics via cross-year POI superimposition-deduplication, coordinate reverse geocoding verification, and ArcGIS-based polygon boundary delineation, effectively compensating for traditional method deficiencies and realizing standardized attribute-spatial data binding. Applying this framework, 599 brownfield sites (9234.83 ha) in Mianyang were identified and classified into four functional categories. Spatial statistical analysis reveals their concentration in Fucheng District and beyond the Second Ring Road, dominance of large-scale plots, and functional differentiation with nearly half remaining polluting industrial land. This study establishes a standardized, replicable process for Third Line Construction cities and a quantitative paradigm for brownfield precise governance.

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