Deep learning for spatio-temporal data mining: A survey
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 …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
[HTML][HTML] Incorporation of AIS data-based machine learning into unsupervised route planning for maritime autonomous surface ships
Abstract Maritime Autonomous Surface Ships (MASS) are deemed as the future of maritime
transport. Although showing attractiveness in terms of the solutions to emerging challenges …
transport. Although showing attractiveness in terms of the solutions to emerging challenges …
A graph-based approach for trajectory similarity computation in spatial networks
Trajectory similarity computation is an essential operation in many applications of spatial
data analysis. In this paper, we study the problem of trajectory similarity computation over …
data analysis. In this paper, we study the problem of trajectory similarity computation over …
TrajGAT: A graph-based long-term dependency modeling approach for trajectory similarity computation
Computing trajectory similarities is a critical and fundamental task for various spatial-
temporal applications, such as clustering, prediction, and anomaly detection. Traditional …
temporal applications, such as clustering, prediction, and anomaly detection. Traditional …
Self-supervised representation learning for geographical data—A systematic literature review
Self-supervised representation learning (SSRL) concerns the problem of learning a useful
data representation without the requirement for labelled or annotated data. This …
data representation without the requirement for labelled or annotated data. This …
Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps
The shape of a geospatial object is an important characteristic and a significant factor in
spatial cognition. Existing shape representation methods for vector-structured objects in the …
spatial cognition. Existing shape representation methods for vector-structured objects in the …
Ship-handling behavior pattern recognition using AIS sub-trajectory clustering analysis based on the T-SNE and spectral clustering algorithms
M Gao, GY Shi - Ocean Engineering, 2020 - Elsevier
Automatic identification system (AIS) trajectory data are collected from multiple sensors that
record dynamic and static ship information. AIS sequences (and records) are affected by …
record dynamic and static ship information. AIS sequences (and records) are affected by …
TAD: A trajectory clustering algorithm based on spatial-temporal density analysis
Y Yang, J Cai, H Yang, J Zhang, X Zhao - Expert Systems with Applications, 2020 - Elsevier
In this paper, a novel trajectory clustering algorithm-TAD-is proposed to extract trajectory
Stays based on spatial-temporal density analysis of data. Two new metrics-NMAST …
Stays based on spatial-temporal density analysis of data. Two new metrics-NMAST …
GRLSTM: trajectory similarity computation with graph-based residual LSTM
The computation of trajectory similarity is a crucial task in many spatial data analysis
applications. However, existing methods have been designed primarily for trajectories in …
applications. However, existing methods have been designed primarily for trajectories in …
Trajectory similarity learning with auxiliary supervision and optimal matching
Trajectory similarity computation is a core problem in the field of trajectory data queries.
However, the high time complexity of calculating the trajectory similarity has always been a …
However, the high time complexity of calculating the trajectory similarity has always been a …