An Adaptive Transformer-Based Language-Model Framework for Assessing Urban Expansion

Urban expansion is a key driver of land-use change and environmental pressure in rapidly urbanizing regions. Existing assessments of urban expansion often rely on predefined indicator systems and fixed weighting schemes, which limits their adaptability to evolving research priorities and regional contexts. This study develops an adaptive framework for urban expansion assessment by integrating a transformer-based language model with multi-source spatial data. A BERT-based semantic extraction process is used to identify relevant indicators and derive their relative weights from the scientific literature, enabling the construction of a literature-driven Urban Expansion Index (UEI). The framework is applied to the Central Plains Mega-city Region (CPMR), China, to examine spatial patterns and temporal dynamics of urban expansion between 2010 and 2020. Results show that UEI is primarily driven by land-use expansion indicators, while socioeconomic, infrastructure, and environmental indicators jointly reflect the multidimensional nature of expansion processes. Spatial patterns reveal a persistent concentration of high expansion intensity in core cities, alongside heterogeneous environmental responses and gradual outward growth. Changes in UEI display weaker spatial coherence than static levels, indicating differentiated local expansion dynamics. Local spatial autocorrelation analysis further identifies shifting clusters of urban expansion intensity, suggesting a reorganization of expansion centers within the agglomeration over time. By linking transformer-based indicator extraction with spatial analysis, this study advances urban expansion assessment beyond outcome-oriented mapping toward a more adaptive and knowledge-informed approach. The proposed framework is transferable to other mega-city regions and provides a useful tool for supporting territorial spatial planning and sustainable urban development.