Deep Learning for Spatio-Temporal Data Mining: A Survey

By Senzhang Wang, Jiannong Cao, Philip S. Yu. IEEE Transactions on Knowledge and Data Engineering, 2019

797 citations recorded by the source corpus.

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real-world applications including human mobility understanding, smart transportation, urban planning, public safety, health care and environmental management. As the number, volume and resolution of spatio-temporal data increase rapidly, traditional data mining methods, especially statistics-based methods for dealing with such data are becoming overwhelmed. Recently deep learning models such as recurrent neural network (RNN) and convolutional neural network (CNN) have achieved remarkable success in many domains due to the powerful ability in automatic feature representation learning, and are also widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, anomaly detection and classification. In this paper, we provide a comprehensive review of recent progress in applying deep learning techniques for STDM. We first categorize the spatio-temporal data into five different types, and then briefly introduce the deep learning models that are widely used in STDM. Next, we classify existing literature based on the types of spatio-temporal data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, on-demand service, climate & weather analysis, human mobility, location-based social network, crime analysis, and neuroscience. Finally, we conclude the limitations of current research and point out future research directions.

Subfields: Building and Construction, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a54c647f6db73621ec496ea86355726161c0898d

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Citations in the corpus

A Survey on Deep Learning for Human Mobility (2020, background): “A few surveys discuss DL approaches to spatio-temporal data mining [202], tasks related to the smart city ecosystem [35] or traffic-related issues [90, 205], covering some aspects related to human mobility but without a specific focus on the challenges and solutions to predict or generate human move”
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, background): “A comprehensive survey on harnessing deep learning models for spatio-temporal data mining is given in [40].”
U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation (2024, result): “The results from our experiments clearly indicate that our method exhibits superior generative performance compared to other state-of-the-art models in the field.”
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023, background): “Wang et al. [3] conducted a review of deep learning techniques for spatio-temporal data mining involving a series of STGNNs in predictive learning up to 2020.”
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “[17] follow and extend the work in [29] and classify the spatio-temporal data into five types: event data, trajectory data, point reference data, raster data and videos.”
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “Indeed, much time series data is spatial-temporal in nature, with different variables in the series capturing information about different locations – space – too, meaning it encapsulates not only time information but also spatial relationships [11].”