Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations

Mingzhi Hu, Zhuoyun Zhong, Xin Zhang, Yanhua Li, Yiqun Xie, Xiaowei Jia, Xun Zhou, Jun Luo

Abstract: Advancements in mobile sensing, data mining, and artificial intelligence have revolutionized the collection and analysis of Human-generated Spatial-Temporal Data (HSTD), paving the way for diverse applications across multiple domains. However, previous works have primarily focused on designing task-specific models for different problems, which lack transferability and generalizability when confronted with diverse HSTD. Additionally, these models often require a large amount of labeled data for optimal performance. While pre-trained models in Natural Language Processing (NLP) and Computer Vision (CV) domains have showcased impressive transferability and generalizability, similar efforts in the spatial-temporal data domain have been limited. In this paper, we take the lead and introduce the Spatial-Temporal Pre-Training model, $i.e$., STPT, which is connected with a self-supervised learning task, to address these limitations. STPT enables the creation of robust and versatile representations of HSTD. We validate our framework using real-world data and demonstrate its efficacy through two downstream tasks, $i.e$., trajectory classification and driving activity identification $(e.g$., identifying seeking $vs$. serving behaviors in taxi trajectories). Our results achieve an accuracy of 83.125% (16.2% higher than the average baseline) for human mobility identification and an accuracy of 77.88% (13.0% higher than the average baseline) for the human activity identification task. These outcomes underscore the potential of our pre-trained model for diverse downstream applications within the spatial-temporal data domain.