In the post-industrial era, many cities have experienced the decline of heavy industry and traditional manufacturing, leading to the widespread emergence of brownfields. These often cluster geographically, forming “brownfield clusters” characterized by shared spatial and functional traits. Our research examined these phenomena within 10 resource-exhausted cities in China, employing kernel density analysis to explore the spatial dynamics within and among these clusters and their urban contexts. We identified three distinct spatial relationships between brownfield clusters and their host cities (coupling, juxtaposition, and encircling), with a detailed case study in Huangshi City further classifying the clusters into five categories based on their dominant factors, spatial morphologies, types of brownfields, and internal dynamics. The study reveals that the spatial configurations of brownfield clusters are significantly influenced by geographic features, transportation infrastructure, and policy frameworks. Based on these findings, we propose targeted regeneration strategies for each cluster type. This research not only enhances our understanding of brownfield challenges and opportunities in China’s resource-exhausted cities but also serves as a valuable reference for other cities and regions worldwide facing similar challenges.

Brownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogeneity of surface features poses significant challenges for identifying various types of brownfields across entire urban areas. To address these challenges, this study proposes a “Target–Classification–Modification” (TCM) method for brownfield identification, which was applied to Tangshan City, China. This method consists of a three-stage process: target area localization, visual interpretation and classification, and site-level modification. It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. The results for Tangshan show that (1) the overall accuracy of the TCM method reached 84.9%; (2) a total of 1706 brownfield sites were identified, including 422 raw-material mining sites, 576 raw-material manufacturing sites, and 708 non-raw-material manufacturing sites; (3) subtype analysis revealed distinct spatial distribution and morphological patterns, driven by resource endowments, transportation networks, and industrial space organization. The TCM method improved the identification efficiency by 34.7% through precise target-area localization. It offers well-defined criteria to distinguish different brownfield subtypes. In addition, it employs a multi-approach strategy to determine the abandonment status, further enhancing accuracy. This method is scalable and widely applicable, providing support for urban-scale brownfield research and practice.