Dynamic Monitoring of High-Rise Building Areas in Xiong’an New Area Using Temporal Change-Aware U-Net

High-rise building areas (HRBs), a key urban land-cover type defined by distinct morphological and functional characteristics, play a critical role in urban development. Their spatial distribution and temporal dynamics serve as essential indicators for quantifying urbanization and analyzing the evolution of urban spatial structure. This study addresses the dynamic monitoring needs of HRBs by developing a temporal change detection model, TCA-Unet (Temporal Change-Aware U-Net), based on a temporal change-aware attention module. The model adopts a dual-path design, combining a temporal attention encoder and a change-aware encoder. By explicitly modeling temporal difference features, it captures change information in temporal remote sensing images. It incorporates a multi-level weight generation mechanism that dynamically balances temporal features and change-aware features through an adaptive fusion strategy. This mechanism effectively integrates temporal context and enhances the model’s ability to capture long-term temporal dependencies. Using the Xiong’an New Area and its surrounding regions as the study area, experiments were conducted using Sentinel-2 time-series imagery from 2017 to 2024. The results demonstrate that the proposed model outperforms existing approaches, achieving an overall accuracy (OA) of 90.98%, an F1 score of 82.63%, and a mean intersection over union (mIoU) of 72.22%. Overall, this study provides an effective tool for extracting HRBs for dynamic monitoring and offers valuable guidance for urban development and regulation.