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Applied Sciences
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Remote Sensing in the Monitoring of Critical Infrastructures
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Remote Sensing in the Monitoring of Critical Infrastructures

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 3190

Special Issue Editor

Prof. Dr. Dursun Zafer Seker

E-Mail Website
Guest Editor
Department of Geomatics Engineering, ITU Faculty of Civil Engineering, 34469 Istanbul, Turkey
Interests: remote sensing; GIS; photogrammetry; machine and deep learning

Special Issue Information

Dear Colleagues,

Critical infrastructures are generally physical and cyber-based systems. They include telecommunications, energy, transportation, water transmission systems, and emergency services, together with a vast network of highways, connecting bridges and tunnels, railways, utilities, and buildings necessary to maintain routine daily life. Therefore, they need to be monitored and protected from accidents, natural and human-induced disasters, and other malicious activities. The need for monitoring the mentioned critical infrastructures increasingly requires the use of advanced techniques. In recent years, the advancements and improvements in technologies such as remote sensing, photogrammetry, GNSS, LiDAR, SAR, and UAVs serve as important and useful tools in providing data and appropriate methodology for monitoring these features. Along with these technologies, it is seen that successful and accurate results are obtained with novel methods such as machine and deep learning.

This Special Issue aims to share the recent state-of-the-art research, achievements, and reviews on monitoring critical infrastructure using remote sensing with the research community. For this issue, we especially welcome papers that use data sources such as remote sensing, photogrammetry, GNSS, LiDAR, SAR, and UAVs together with novel methods such as machine learning and deep learning.

Prof. Dr. Dursun Zafer Seker
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 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. Applied Sciences 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

  • critical infrastructures
  • monitoring
  • remote sensing
  • photogrammetry
  • LiDAR
  • UAV
  • machine learning
  • deep learning

Published Papers (1 paper)

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Research

18 pages, 11200 KiB  
Article
Improving Road Segmentation by Combining Satellite Images and LiDAR Data with a Feature-Wise Fusion Strategy
by Ozan Ozturk, Mustafa Serkan Isik, Martin Kada and Dursun Zafer Seker
Appl. Sci. 2023, 13(10), 6161; https://doi.org/10.3390/app13106161 - 17 May 2023
Cited by 5 | Viewed by 2656
Abstract
Numerous deep learning techniques have been explored in pursuit of achieving precise road segmentation; nonetheless, this task continues to present a significant challenge. Exposing shadows and the obstruction of objects are the most important difficulties associated with road segmentation using optical image data [...] Read more.
Numerous deep learning techniques have been explored in pursuit of achieving precise road segmentation; nonetheless, this task continues to present a significant challenge. Exposing shadows and the obstruction of objects are the most important difficulties associated with road segmentation using optical image data alone. By incorporating additional data sources, such as LiDAR data, the accuracy of road segmentation can be improved in areas where optical images are insufficient to segment roads properly. The missing information in spectral data due to the object blockage and shadow effect can be compensated by the integration of 2D and 3D information. This study proposes a feature-wise fusion strategy of optical images and point clouds to enhance the road segmentation performance of a deep learning model. For this purpose, high-resolution satellite images and airborne LiDAR point cloud collected over Florida, USA, were used. Eigenvalue-based and geometric 3D property-based features were calculated based on the LiDAR data. These optical images and LiDAR-based features were used together to train, end-to-end, a deep residual U-Net architecture. In this strategy, the high-level features generated from optical images were concatenated with the LiDAR-based features before the final convolution layer. The consistency of the proposed strategy was evaluated using ResNet backbones with a different number of layers. According to the obtained results, the proposed fusion strategy improved the prediction capacity of the U-Net models with different ResNet backbones. Regardless of the backbone, all models showed enhancement in prediction statistics by 1% to 5%. The combination of optical images and LiDAR point cloud in the deep learning model has increased the prediction performance and provided the integrity of road geometry in woodland and shadowed areas. Full article
(This article belongs to the Special Issue Remote Sensing in the Monitoring of Critical Infrastructures)
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