Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
11-2025
Abstract
Traditional deep learning methods and econometric models have played a crucial role in the field of data mining, particularly in the prediction of socioeconomic outcomes. However, socio-economic 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 effective and stable 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 socio-economic information and the effectiveness of our transfer learning-based framework in extracting valuable insights from satellite imagery to support socioeconomic policy decisions. The code was released in https://github.com/xiachan254/PredSocecOut.
Keywords
Transfer learning, Data mining, Pretrained model, Remote sensing, Socioeconomic outcomes
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Analytics for Business, Consumer and Social Insights
Publication
Proceedings of the Twenty-Second Pacific Rim International Conference on Artificial Intelligence (PRICAI 2025), Wellington, New Zealand, 2025 November 17-21
First Page
1
Last Page
8
Embargo Period
12-3-2025
Citation
TANG, Yang; CHENG, Shih-Fen; ZHU, Yunqiang; YANG, Yichen; and ZOU, Zhiqiang.
Predict social economic outcomes by transferred knowledge with satellite imagery. (2025). Proceedings of the Twenty-Second Pacific Rim International Conference on Artificial Intelligence (PRICAI 2025), Wellington, New Zealand, 2025 November 17-21. 1-8.
Available at: https://ink.library.smu.edu.sg/sis_research/10539
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://pricai.org/2025/