Semantic segmentation for extracting buildings and roads, from unmanned aerial vehicle (UAV) remote sensing images by deep learning becomes a more efficient and convenient method than traditional manual segmentation in surveying and mapping field. In order to make the model lightweight and improve the model accuracy, A Lightweight and Efficient Network implemented using Dual Context modules (LEDCNet) for Buildings and Roads from UAV Aerial Remote Sensing Images is proposed. The proposed network adopts an encoder-decoder architecture in which a Lightweight Densely Connected Network (LDCNet) is developed as the encoder. In the decoder part, the dual multi-scale context modules which consist of the Atrous Spatial Pyramid Pooling module (ASPP) and the Object Contextual Representation module (OCR) are designed to capture more context information from feature maps of UAV remote sensing images. Between ASPP and OCR, a Feature Pyramid Network (FPN) module is used to and fuse multi-scale features extracting from ASPP. A private dataset of remote sensing images taken by UAV which contains 2431 training sets, 945 validation sets, and 475 test sets is constructed. The proposed model performs well on this dataset, with only 1.4M parameters and 5.48G floating-point operations (FLOPs), achieving an mean intersection-over-union ratio (mIoU) of 71.12%. More extensive experiments on the public LoveDA dataset and CITY-OSM dataset to further verify the effectiveness of the proposed model with excellent results on mIoU of 65.27% and 74.39%, respectively. The source code will be made available on https://github.com/GtLinyer/LEDCNet .

翻译：利用深度学习从无人机遥感图像中语义分割提取建筑物和道路，相比测绘领域传统的人工分割方法，已成为一种更高效且便捷的方式。为使模型轻量化并提升精度，本文提出了一种基于双上下文模块的轻量级高效网络（LEDCNet），用于从无人机航拍遥感图像中提取建筑物和道路。该网络采用编码器-解码器架构，其中开发了一种轻量级密集连接网络（LDCNet）作为编码器。在解码器部分，设计了由空洞空间金字塔池化模块（ASPP）和对象上下文表示模块（OCR）构成的双多尺度上下文模块，以从无人机遥感图像特征图中捕获更多上下文信息。在ASPP与OCR之间，引入特征金字塔网络（FPN）模块，用于融合来自ASPP的多尺度特征。本研究构建了一个包含2431张训练集、945张验证集和475张测试集的无人机遥感图像私有数据集。所提模型在该数据集上表现优异，参数量仅为1.4M，浮点运算量（FLOPs）为5.48G，平均交并比（mIoU）达到71.12%。在公开的LoveDA数据集和CITY-OSM数据集上进行更广泛的实验，进一步验证了该模型的有效性，分别取得了65.27%和74.39%的mIoU优异结果。源代码将在https://github.com/GtLinyer/LEDCNet上公开。