  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.
