Resumen
The extraction of roads and centerlines from aerial imagery is considered an important topic because it contributes to different fields, such as urban planning, transportation engineering, and disaster mitigation. Many researchers have studied this topic as a two-separated task that affects the quality of extracted roads and centerlines because of the correlation between these two tasks. Accurate road extraction enhances accurate centerline extraction if these two tasks are processed simultaneously. This study proposes a multitask learning scheme using a gated deep convolutional neural network (DCNN) to extract roads and centerlines simultaneously. The DCNN is composed of one encoder and two decoders implemented on the U-Net backbone. The decoders are assigned to extract roads and centerlines from low-resolution feature maps. Before extraction, the images are processed within an encoder to extract the spatial information from a complex, high-resolution image. The encoder consists of the residual blocks (Res-Block) connected to a bridge represented by a Res-Block, and the bridge connects the two identical decoders, which consists of stacking convolutional layers (Conv.layer). Attention gates (AGs) are added to our model to enhance the selection process for the true pixels that represent road or centerline classes. Our model is trained on a dataset of high-resolution aerial images, which is open to the public. The model succeeds in efficiently extracting roads and centerlines compared with other multitask learning models.