Traditional deep learning methods and econometric model have played a crucial role in the field of data mining, particularly in the prediction of socioeconomic 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 topological features to generate reasonable road network types. Given the unique characteristics of road networks, we have constructed and fine-tuned a hybrid pre-trained model that combines ResNet50 and Vision Transformer architectures for the transfer learning task. Through extensive experiments conducted across multiple regions, we demonstrated that our approach outperforms state-of-the-art methods in this field. This work highlights the potential of leveraging road network types as a proxy for socioeconomic information 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 .