The aircraft system has recently gained its reputation as a reliable and efficient tool for sensing and parsing aerial scenes.However,accurate and fast semantic segmentation of high-resolution aerial images for remote sensing applications is still facing three challenges:the require-ments for limited processing resources and low-latency operations based on aerial platforms,the balance between high accuracy and real-time efficiency for model performance,and the confusing objects with large intra-class variations and small inter-class differences in high-resolution aerial images.To address these issues,a lightweight and dual-path deep convolutional architecture,namely Aerial Bilateral Segmentation Network (Aerial-BiSeNet),is proposed to perform real-time segmentation on high-resolution aerial images with favorable accuracy.Specifically,inspired by the receptive field concept in human visual systems,Receptive Field Module (RFM) is proposed to encode rich multi-scale contextual information.Based on channel attention mechanism,two novel modules,called Feature Attention Module (FAM) and Channel Attention based Feature Fusion Module (CAFFM) respectively,are proposed to refine and combine features effectively to boost the model performance.Aerial-BiSeNet is evaluated on the Potsdam and Vaihingen data-sets,where leading performance is reported compared with other state-of-the-art models,in terms of both accuracy and efficiency.