# MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images Source: [MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:10:48.263Z Authors: Rui Li, Chenxi Duan, Shunyi Zheng, Ce Zhang, Peter M. Atkinson Published: 2020-07-26T08:56:47.000Z ## Abstract Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image segmentation with high accuracy. In this Letter, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected and asymmetric-convolution-based U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images. Our design has the following advantages: (1) The multi-scale skip connections combine and realign semantic features contained in both low-level and high-level feature maps; (2) the asymmetric convolution block strengthens the feature representation and feature extraction capability of a standard convolution layer. Experiments conducted on two remotely sensed datasets captured by different satellite sensors demonstrate that the proposed MACU-Net transcends the U-Net, U-NetPPL, U-Net 3+, amongst other benchmark approaches. Code is available at https://github.com/lironui/MACU-Net. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Road Segmentation on SYSU-HiRoads (test)](): Road Segmentation; SYSU-HiRoads (test); Recall: 72.16. - [Road Segmentation on CHN6-CUG](): Road Segmentation; CHN6-CUG; mIoU: 43.26. ## Original sources and implementation [Original paper]() [PDF]()