GLC‑Net: Global‑Local Collaborative Network for Remote Sensing Image Segmentation

Intelligent interpretation of high‑resolution remote sensing imagery is a fundamental challenge in aerospace information processing. Complex ground environments such as construction and demolition （C&D） waste landfills exemplify the need for robust segmentation models that can handle diverse spatial and spectral patterns. Conventional convolutional neural networks （CNNs） are limited by their local receptive fields， whereas Transformer‑based architectures often lose fine spatial detail， resulting in incomplete delineation of heterogeneous remote sensing targets. To address these issues， we propose a global‑local collaborative network （GLC‑Net）， which is designed for intelligent remote sensing image segmentation. The model integrates an efficient Transformer block to capture global dependencies and a local enhancement block to refine structural details. Furthermore， a multi‑scale spatial aggregation and enhancement （MSAE） module is introduced to strengthen contextual representation and suppress background noise. Deep supervision facilitates hierarchical feature learning. Experiments on two high‑resolution remote sensing datasets （Changping and Daxing） demonstrate that GLC‑Net surpasses state‑of‑the‑art baselines by 1.5%—3.2% in mean intersection over union （mIoU）， while achieving superior boundary precision and semantic consistency. These results confirm that global‑local collaborative modeling provides an effective pathway for intelligent remote sensing image segmentation in aerospace environmental monitoring.