HGRHDNet: Hierarchical Gated Residual Fusion and High-Frequency Guided Deformable Upsampler Network for Boundary-Enhanced Binary Urban Green Space Extraction

High-resolution remote sensing imagery provides valuable data support for accurate binary urban green space extraction. However, due to complex urban backgrounds, existing methods still face challenges in accurately delineating green space boundaries and preserving fine-scale spatial details. To address these issues, this study proposes a novel network, termed Hierarchical Gated Residual Fusion and High-Frequency Guided Deformable Upsampler Network (HGRHDNet), for boundary-enhanced urban green space segmentation. The proposed framework adopts ConvNeXt-L as the encoder backbone and incorporates a Hierarchical Gated Residual Fusion Decoder (HGRFD) to adaptively fuse multi-scale features through dynamic weighting and residual feature propagation. In addition, a High-Frequency Guided Deformable Upsampler (HFGDU) is introduced to enhance high-frequency detail reconstruction and cross-resolution feature alignment, thereby improving boundary localization accuracy. The proposed method was evaluated on three public datasets with different spatial resolutions and spectral characteristics, including WHDLD, UGS-1m, and UBGG. Experimental results show that HGRHDNet achieves Boundary Intersection over Union (BIoU) values of 43.82%, 18.22%, and 64.55% on the three datasets, respectively, consistently outperforming state-of-the-art methods. Both quantitative and qualitative analyses demonstrate that HGRHDNet effectively preserves narrow gaps between adjacent green spaces, elongated vegetation structures, and fragmented green space patches while reducing boundary ambiguity in complex urban environments. These results indicate that HGRHDNet provides a robust and effective solution for high-resolution urban green space extraction and has considerable potential for applications in urban ecological assessment, green space inventory, and sustainable urban planning.