Special Issue "Big Data Challenges in Smart Cities"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 30 November 2018

Special Issue Editor

Guest Editor
Prof. Dr. Robert Laurini

Knowledge Systems Institute, Chicago, IL, USA; Institut National des Sciences Appliquées de Lyon, INSA Lyon, France
Website | E-Mail

Special Issue Information

Dear Colleagues,

Each day, local authorities are collecting zillions of bytes of data and they urgently long whether those data can be useful in decision-making. The so-called big data are coming from various sources, such as from real-time sensors for air pollution, traffic management and energy management, video-surveillance, administrative forms, GIS 2D or 3D data, GPS tracks, aerial photos, videos from drones, etc., without forgetting crowdsourcing for VGI and public participation.

For local administrators and elected officials in smart cities, the optimal use of their big data is very important, since ICT must not be the only the main resource, but rather the overall core of their smart governance.

Various challenges are emerging: How to structure big data? How to combine them efficiently? How to query them? How to extract knowledge? How to extract salient features, determining patterns and trends? How to combine them with deep learning? How to visualize them? How to integrate them into urban dashboards? How to preserve privacy? What are the best strategies for storing them? Surely many other challenges will appear.

In this Special Issue, we are especially interested in original papers dealing with these aspects, and/or describing novel experiences, as well as enriching big data theories with geographic aspects.

Prof. Dr. Robert Laurini
Guest Editor

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 papers will be 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. Data is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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

  • big data
  • smart cities
  • smart governance
  • urban knowledge extraction
  • geographic knowledge

Published Papers (1 paper)

View options order results:
result details:
Displaying articles 1-1
Export citation of selected articles as:

Review

Open AccessReview Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review
Received: 15 June 2018 / Revised: 5 July 2018 / Accepted: 18 July 2018 / Published: 24 July 2018
PDF Full-text (1061 KB) | HTML Full-text | XML Full-text
Abstract
Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms
[...] Read more.
Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images. Full article
(This article belongs to the Special Issue Big Data Challenges in Smart Cities)
Figures

Figure 1

Back to Top