Fine-grained building function identification (BFI) is essential for sustainable urban development, land-use analysis, and data-driven spatial planning. Recent progress in fully supervised semantic segmentation has advanced multimodal BFI; however, most approaches still rely on static fusion and lack explicit multi-scale alignment. As a result, they struggle to adaptively integrate heterogeneous inputs and suppress cross-modal interference, which constrains representation learning. To overcome these limitations, we propose BuildFunc-MoE, an adaptive multimodal Mixture-of-Experts (MoE) network built on an effective end-to-end Swin-UNet backbone. The model treats high-resolution remote sensing imagery as the primary input and integrates auxiliary geospatial data such as nighttime light imagery, DEM, and point-of-interest information. An Adaptive Multimodal Fusion Gate (AMMFG) first refines auxiliary features into informative fused representations, which are then combined with the primary modality and passed through multi-scale Swin-MoE blocks that extend standard Swin Transformer blocks with MoE routing. This enables fine-grained, dynamic fusion and alignment between primary and auxiliary modalities across feature scales. BuildFunc-MoE further introduces a Shared Task-Expert Module (STEM), which extends the MoE framework to share experts between the main BFI task and auxiliary tasks (road extraction, green space segmentation, and water body detection), enabling parameter-level transfer. This design enables complementary feature learning, where structural and contextual information jointly enhance the discrimination of building functions, thereby improving identification accuracy while maintaining model compactness. Experiments on the proposed Wuhan-BF multimodal dataset show that, under identical supervision, BuildFunc-MoE outperforms the strongest multimodal baseline by over 2% on average across metrics. Both PyTorch and LuoJiaNET implementations validate its effectiveness, while the latter achieves higher accuracy and faster inference through optimized computation. Overall, BuildFunc-MoE offers a scalable solution for fine-grained BFI with strong potential for urban planning and sustainable governance.

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.

Urban well-being in China emerges from spatially uneven configurations of development intensity, environmental pressure, governance input, public-service capacity, land-use transformation, and urban–rural conditions. Using a balanced panel of 294 Chinese prefecture-level cities from 2004 to 2023 (5880 city–year observations), this study combines spatial mapping, Local Moran’s I, Getis–Ord Gi*, and two-way fixed-effects (TWFE) models to examine how governance-related conditions and ecological and environmental pressure are associated with resident well-being. Prefecture-level cities are treated as urban–rural territorial governance units encompassing urban cores, peri-urban areas, and surrounding county-level jurisdictions. Spatial diagnostics reveal non-identical clustering of resource and environmental intensity (REI), government regulation and investment (GRI), ecological and environmental pressure (EEP), and resident well-being (RWB). REI is positively associated with EEP, and this association remains positive after excluding observations containing ordinary statistical completion. EEP is negatively associated with RWB in the full-sample TWFE and one-year-lagged specifications, but the association weakens in the 2014–2023 and restricted samples, indicating temporal and sample boundaries rather than a stable mediating mechanism. GRI shows contrasting cross-city and within-city patterns, consistent with a distinction between governance capacity and pressure-responsive adjustment. Dimension-level results further show that EEP is negatively associated with rural disposable income, whereas medical service capacity is positively associated with rural disposable income. Overall, the study provides a spatially grounded account of heterogeneous governance–environment–welfare relationships across Chinese prefecture-level territories.