Aiming at the unsatisfactory road extraction of complex scenes and the fast learning ability of Extreme Learning Machine (ELM) in high-resolution remote sensing images, an ELM-based urban road extraction method is proposed. Firstly, the improved Cuckoo Search algorithm (CS) is used to adaptively select the number of hidden layer nodes of the ELM, in order to improve the stability of the model. Secondly, the discriminant information in the data sample is introduced to make up for the insufficient ELM learning, thus improving the ELM classification performance. Finally, the mathematical morphology processing is used to optimize the extracted road so as to obtain the final road extraction effect. The road extraction test results of remote sensing image show that the proposed method not only enhances the stability of the network, but also improves the accuracy of road extraction, and can extract road information better.