# TNet: Terrace Convolutional Decoder Network for Remote Sensing Image Semantic Segmentation Source: [TNet: Terrace Convolutional Decoder Network for Remote Sensing Image Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:32:44.589Z Authors: Chengqian Dai, Yonghong Guo, Hongzhao Xiang, Yigui Luo Published: 2025-08-06T03:44:30.000Z ## Abstract In remote sensing, most segmentation networks adopt the UNet architecture, often incorporating modules such as Transformers or Mamba to enhance global-local feature interactions within decoder stages. However, these enhancements typically focus on intra-scale relationships and neglect the global contextual dependencies across multiple resolutions. To address this limitation, we introduce the Terrace Convolutional Decoder Network (TNet), a simple yet effective architecture that leverages only convolution and addition operations to progressively integrate low-resolution features (rich in global context) into higher-resolution features (rich in local details) across decoding stages. This progressive fusion enables the model to learn spatially-aware convolutional kernels that naturally blend global and local information in a stage-wise manner. We implement TNet with a ResNet-18 encoder (TNet-R) and evaluate it on three benchmark datasets. TNet-R achieves competitive performance with a mean Intersection-over-Union (mIoU) of 85.35\\% on ISPRS Vaihingen, 87.05\\% on ISPRS Potsdam, and 52.19\\% on LoveDA, while maintaining high computational efficiency. Code is publicly available. ## 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. ## Original sources and implementation [Original paper]() [PDF]()