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 …
A survey on trajectory data management, analytics, and learning
Recent advances in sensor and mobile devices have enabled an unprecedented increase
in the availability and collection of urban trajectory data, thus increasing the demand for …
in the availability and collection of urban trajectory data, thus increasing the demand for …
[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 …
Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery
Owing to the space–air–ground integrated networks (SAGIN), seaborne shipping has
attracted increasing interest in the research on the motion behavior knowledge extraction …
attracted increasing interest in the research on the motion behavior knowledge extraction …
[HTML][HTML] Spatiotemporal data mining: a survey on challenges and open problems
Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay
between space and time. Several available surveys capture STDM advances and report a …
between space and time. Several available surveys capture STDM advances and report a …
Data collection and quality challenges for deep learning
Software 2.0 refers to the fundamental shift in software engineering where using machine
learning becomes the new norm in software with the availability of big data and computing …
learning becomes the new norm in software with the availability of big data and computing …
An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation
To achieve reliable mining results for massive vessel trajectories, one of the most important
challenges is how to efficiently compute the similarities between different vessel trajectories …
challenges is how to efficiently compute the similarities between different vessel trajectories …
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 …
Vehicle trajectory similarity: Models, methods, and applications
The increasing availability of vehicular trajectory data is at the core of smart mobility
solutions. Such data offer us unprecedented information for the development of trajectory …
solutions. Such data offer us unprecedented information for the development of trajectory …
Self-supervised trajectory representation learning with temporal regularities and travel semantics
Trajectory Representation Learning (TRL) is a powerful tool for spatial-temporal data
analysis and management. TRL aims to convert complicated raw trajectories into low …
analysis and management. TRL aims to convert complicated raw trajectories into low …