Semantic segmentation for extracting buildings and roads from uncrewed aerial vehicle (UAV) remote sensing images by deep learning becomes a more efficient and convenient method than traditional manual segmentation in surveying and mapping fields. In order to make the model lightweight and improve the model accuracy, a lightweight network using object attention (LOANet) 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 attention module (OAM) are designed to capture more context information from feature maps of UAV remote sensing images. Between ASPP and OAM, a feature pyramid network (FPN) module is used to fuse multi-scale features extracted 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 basic model performs well on this dataset, with only 1.4M parameters and 5.48G floating point operations (FLOPs), achieving excellent mean Intersection-over-Union (mIoU). Further experiments on the publicly available LoveDA and CITY-OSM datasets have been conducted to further validate the effectiveness of the proposed basic and large model, and outstanding mIoU results have been achieved. All codes are available on https://github.com/GtLinyer/LOANet.