AM-UNet: Road Network Extraction from high-resolution Aerial Imagery Using Attention-Based Convolutional Neural Network

Roads are an essential element of various information systems such as geographic information systems, transportation systems, etc. The main source of road information is remote sensing data as it covers a large amount of area. Despite recent technological advancements precise road information extraction is still a tedious task. In this work, a computational-efficient deep learning architecture AM-Unet is proposed to extract road information from high-resolution aerial imagery. The proposed method alters the design of Unet architecture for the encoder, decoder, and skip connections. These changes enhance the computational efficiency of the decoder to recapture spatial location information. The experiments are performed on complex high-resolution (HR) aerial images and the results are assessed on diverse quantitative parameters. The experimental results are compared to other deep learning methods which reflects the improvement in results on Precision, recall, Acc and F1-score parameters.