Traditional deep learning methods and econometric model have played a crucial role in the field of data mining, particularly in the prediction of soci-oeconomic outcomes. However, socioeconomic information is unable to be directly extracted from remote sensing data. So, in this paper, we propose a method to leverage transfer learning to predict socioeconomic indicators (outcomes) through satellite imagery. Specifically, we use road network types as a proxy for socioeconomic factors, which is more effectively and stably than using nightlight. We have extracted eleven distinct road topologi-cal features to generate reasonable road network types. Given the unique characteristics of road networks, we have constructed and fine-tuned a hy-brid pre-trained model that combines ResNet50 and Vision Transformer ar-chitectures for the transfer learning task. Through extensive experiments conducted across multiple regions, we demonstrated that our approach out-performs state-of-the-art methods in this field. This work highlights the po-tential of leveraging road network types as a proxy for socioeconomic in-formation and the effectiveness of our transfer learning-based framework in extracting valuable insights from satellite imagery to support socioeconomic policy decisions. The code had released in https://github.com/xiachan254/PredSocecOut.