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Navigation and Remote Sensing for Sustainable Development

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (10 May 2021) | Viewed by 4060

Special Issue Editors


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Guest Editor
Department of Civil Engineering, Ryerson University, Toronto M5B 2K3, Canada
Interests: geomatics engineering; satellite navigation and multi-sensor integration for mobile indoor/outdoor mapping, unmanned aerial systems, and autonomous driving

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Guest Editor
Department of Civil Engineering, Faculty of Engineering and Architecture Science, Ryerson University, Toronto, ON M5B 2K3, Canada
Interests: LiDAR data processing; satellite sensor modelling; image segmentation and classification; 3-D modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable environments reflect effective resource management, including the natural environment of the land, water, and air, or the urban environment of buildings, cities, and infrastructure. Sustainability is critical for the human population, not only this generation but also for the generations to come. The transformation from traditional to sustainable environments requires interdisciplinary approaches in order to integrate knowledge and technologies, as well as examine, explore, and critically engage with issues and advances of urban environments. Research challenges in this field range from calibration, data acquisition, fusion, and modelling to efficient information extraction, visualization, and mapping. Therefore, the scope of this Special Issue covers research areas such as multi-sensor integration for sustainable urban environments, remote sensing for urban sustainability, and 3D city modelling, among others. This Special Issue focuses on new developments and applications using, but not limited to, big data analysis, machine learning, optimal data fusion algorithms, and advanced classification methods, among others. Researchers are encouraged to use a wide range of related data such as global navigation satellite systems, inertial measurements, images and LiDAR point cloud data from a single or multiple platforms, etc.

Prof. Dr. Ahmed El-Rabbany
Prof. Dr. Ahmed Shaker
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Multi-source, multi-platform navigation and remote sensing data acquisition, modelling, and analysis;
  • Multi-sensor integration for sustainable environments;
  • Innovative fusion techniques for multi-source geospatial data;
  • Smart cities, urban planning, and disaster response;
  • Multi-sensor integration for assets mapping and infrastructure monitoring;
  • Big data management, processing, analysis, and visualization;
  • Collaborative geospatial data acquisition, mapping, and management.

Published Papers (1 paper)

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Research

22 pages, 19127 KiB  
Article
Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN
by Xuyang Han, Costas Armenakis and Mojgan Jadidi
Sustainability 2021, 13(15), 8162; https://0-doi-org.brum.beds.ac.uk/10.3390/su13158162 - 21 Jul 2021
Cited by 33 | Viewed by 3575
Abstract
Today, maritime transportation represents a substantial portion of international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this paper, we present an enhanced density-based spatial clustering of applications with noise (DBSCAN) method to model vessel [...] Read more.
Today, maritime transportation represents a substantial portion of international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this paper, we present an enhanced density-based spatial clustering of applications with noise (DBSCAN) method to model vessel behaviours based on trajectory point data. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis distance metric, which considers the correlation between the points representing vessel locations. This research proposes applying the clustering method to historical Automatic Identification System (AIS) data using an algorithm to generate a clustering model of the vessels’ trajectories and a model for detecting vessel trajectory anomalies, such as unexpected stops, deviations from regulated routes, or inconsistent speed. Further, an automatic and data-driven approach is proposed to select the initial parameters for the enhanced DBSCAN approach. Results are presented from two case studies using an openly available Gulf of Mexico AIS dataset as well as a Saint Lawrence Seaway and Great Lakes AIS licensed dataset acquired from ORBCOMM (a maritime AIS data provider). These research findings demonstrate the applicability and scalability of the proposed method for modeling more water regions, contributing to situational awareness, vessel collision prevention, safe navigation, route planning, and detection of vessel behaviour anomalies for auto-vessel development towards the sustainability of marine transportation. Full article
(This article belongs to the Special Issue Navigation and Remote Sensing for Sustainable Development)
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