ViL-UNet: Multi-Scale Global–Local Feature Fusion for High-Resolution Remote-Sensing Segmentation

Efficiently extracting and integrating multi-scale global and local information remains a main challenge in remote-sensing semantic-segmentation tasks. Current convolutional neural networks (CNNs) can only capture fine-grained details but cannot model long-range dependencies. In contrast, sequence models, such as Transformer-based and Mamba-based models, have the ability to capture broad context. However, they have limitations in preserving local details. Recent hybrid architectures combine both to balance these strengths, but it is still difficult to fuse multi-scale global and local features in a unified and efficient manner. In this work, we propose ViL-UNet, a dual-branch encoder–decoder network, which addresses this challenge by combining a Vision-LSTM (ViL) global encoder with a CNN-based local encoder. The global encoder captures long-range dependencies across multiple scales. Furthermore, the local encoder preserves fine spatial details. We also design a novel Multi-Scale Fusing Module to integrate these complementary representations by resizing the global and local features to a common spatial grid, concatenating them, and generating a unified feature representation that captures broad contextual cues and fine-grained local information. The decoder applies Efficient Global–Local Attention and Squeeze-and-Excitation. In addition, the skip connections bring the fused multi-scale features back to higher resolutions. To demonstrate ViL-UNet’s effectiveness, we compared it against state-of-the-art methods on the ISPRS Vaihingen and LoveDA Urban benchmarks. Across both datasets, ViL-UNet consistently delivers more accurate semantic segmentation by efficiently capturing multi-scale global and local context in large-scale remote-sensing scenes.