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Big Data Analytics for Secure and Smart Environmental Services

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (25 December 2021) | Viewed by 37658

Special Issue Editors

Edinburgh Napier University, Scotland, United Kingdom
Interests: big data; cyber-security; Internet of Things; learning; smart cities
Special Issues, Collections and Topics in MDPI journals
Prof. Ahmed Al-Dubai
E-Mail Website
Guest Editor
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: big data; cyber-security; Internet of Things; learning; smart cities
Special Issues, Collections and Topics in MDPI journals
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: cybersecurity; information society; networks; blockchain; cryptography; trust; digital identity
Special Issues, Collections and Topics in MDPI journals
Faulty of Engineering, University of Windsor, Windsor, ON N9B3P4, Canada
Interests: computer vision systems for active vehicle safety and driver assistance; machine learning and sensor fusion for autonomous driving; sensor technology; big data analytics for medicine; cross-border security; distributed sensing for industrial monitoring and automation
Special Issues, Collections and Topics in MDPI journals
Xi’an Jiaotong Liverpool University, Suzhou 215123, China
Interests: big data; machine learning; data mining; pattern recognition; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Anhui University, Anhui, Hefei 230039, China
Interests: big data; computer vision; pattern recognition; video analysis; surveillance systems
Prof. Dr. Jin Tang
E-Mail Website
Guest Editor
Anhui University, Anhui, Hefei 230039, China
Interests: big data; computer vision; pattern recognition; video analysis; surveillance systems
PSU Faculty, Prince Sultan University, Riyadh 12435, Saudi Arabia
Interests: remote sensing; big data; deep learning; internet of things; uncertainty modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adel M. Alimi
E-Mail Website
Guest Editor
REGIM-Laboratory, University of Sfax, Sfax, Tunisia
Interests: big data; machine learning; computational intelligence

Special Issue Information

Dear Colleagues,

Today, the volume of remote sensing (RS) data has grown considerably, leading to a new research domain, termed big RS data. Analyzing, modeling, and interpreting RS big data through descriptive, predictive, and prescriptive analytics will aid users to make correct decisions in many fields related to RS. On the other hand, the evolution of Internet of Things (IoT) and cybersecurity technologies is continuing to play an important role in developing smart cities of tomorrow.

The growing combination of big data analytics, IoT, and cybersecurity offer the potential to learn and extract knowledge from heterogeneous RS big data collected from different sources, in order to develop real-world environmental services implemented through secure IoT infrastructures.

This timely Special Issue invites submissions on a range of related topics, including, but not limited to, the following:

  • Novel machine learning algorithms for big RS data (e.g., deep learning, active learning, reinforcement learning, evolutionary computing, etc.);
  • Methods and techniques for collection, cleaning, analyzing, distribution, and visualization of big RS data;
  • Unceratinty modeling in IoT and big RS data;
  • Cybersecurity methods and techniques to enable secure and privacy-assured IoT Big Data analytics for smart environmental services;
  • Infrastructure for IoT applied to big RS data (including cloud and edge computing, high performance computing, emerging hardware architectures, optimization, control, automation, security, etc.);
  • Environmental services, such as regional planning, climate forecasting, crowd management, resources management, land use/cover management, urban sprawl planning, water resource management,
Prof. Amir Hussain
Prof. Ahmed Al-Dubai
Prof. Bill Buchanan
Prof. Jonathan Wu
Prof. Kaizhu Huang
Prof. Bin Luo
Prof. Jin Tang
Dr. Wadii Boulila
Prof. Adel M. Alimi
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. Remote Sensing 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 2700 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

  • Big remote sensing data
  • Smart environmental services (regional planning, climate forecasting, crowd management, resources management, land use/cover management, urban sprawl planning, water resource management, etc.)
  • Internet of Things
  • Novel machine learning algorithms (deep learning, active learning, reinforcement learning, evolutionary computing, constraint satisfaction problems, etc.)
  • Cybersecurity

Published Papers (8 papers)

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26 pages, 2646 KiB  
Article
SmartWater: A Service-Oriented and Sensor Cloud-Based Framework for Smart Monitoring of Water Environments
Remote Sens. 2022, 14(4), 922; https://doi.org/10.3390/rs14040922 - 14 Feb 2022
Cited by 16 | Viewed by 3071
Abstract
Due to the sharp increase in global industrial production, as well as the over-exploitation of land and sea resources, the quality of drinking water has deteriorated considerably. Furthermore, nowadays, many water supply systems serving growing human populations suffer from shortages since many rivers, [...] Read more.
Due to the sharp increase in global industrial production, as well as the over-exploitation of land and sea resources, the quality of drinking water has deteriorated considerably. Furthermore, nowadays, many water supply systems serving growing human populations suffer from shortages since many rivers, lakes, and aquifers are drying up because of global climate change. To cope with these serious threats, smart water management systems are in great demand to ensure vigorous control of the quality and quantity of drinking water. Indeed, water monitoring is essential today since it allows to ensure the real-time control of water quality indicators and the appropriate management of resources in cities to provide an adequate water supply to citizens. In this context, a novel IoT-based framework is proposed to support smart water monitoring and management. The proposed framework, named SmartWater, combines cutting-edge technologies in the field of sensor clouds, deep learning, knowledge reasoning, and data processing and analytics. First, knowledge graphs are exploited to model the water network in a semantic and multi-relational manner. Then, incremental network embedding is performed to learn rich representations of water entities, in particular the affected water zones. Finally, a decision mechanism is defined to generate a water management plan depending on the water zones’ current states. A real-world dataset has been used in this study to experimentally validate the major features of the proposed smart water monitoring framework. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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22 pages, 3486 KiB  
Article
Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images
Remote Sens. 2022, 14(3), 613; https://doi.org/10.3390/rs14030613 - 27 Jan 2022
Cited by 30 | Viewed by 6100
Abstract
Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development in detecting roads in high-resolution (HR) satellite images based on semantic segmentation. However, [...] Read more.
Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development in detecting roads in high-resolution (HR) satellite images based on semantic segmentation. However, the objects being segmented in such images are of small size, and not all the information in the images is equally important when making a decision. This paper proposes a novel approach to road detection based on semantic segmentation and edge detection. Our approach aims to combine these two techniques to improve road detection, and it produces sharp-pixel segmentation maps, using the segmented masks to generate road edges. In addition, some well-known architectures, such as SegNet, used multi-scale features without refinement; thus, using attention blocks in the encoder to predict fine segmentation masks resulted in finer edges. A combination of weighted cross-entropy loss and the focal Tversky loss as the loss function is also used to deal with the highly imbalanced dataset. We conducted various experiments on two datasets describing real-world datasets covering the three largest regions in Saudi Arabia and Massachusetts. The results demonstrated that the proposed method of encoding HR feature maps effectively predicts sharp segmentation masks to facilitate accurate edge detection, even against a harsh and complicated background. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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26 pages, 5817 KiB  
Article
AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images
Remote Sens. 2021, 13(24), 5109; https://doi.org/10.3390/rs13245109 - 16 Dec 2021
Cited by 7 | Viewed by 2078
Abstract
The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject-unrelated tampering” of [...] Read more.
The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject-unrelated tampering” of remote sensing images. However, the existing subject-sensitive hashing still has a large deficiency in robustness. In this paper, we propose a novel attention-based asymmetric U-Net (AAU-Net) for the subject-sensitive hashing of remote sensing (RS) images. Our AAU-Net demonstrates obvious asymmetric structure characteristics, which is important to improve the robustness of features by combining the attention mechanism and the characteristics of subject-sensitive hashing. On the basis of AAU-Net, a subject-sensitive hashing algorithm is developed to integrate the features of various bands of RS images. Our experimental results show that our AAU-Net-based subject-sensitive hashing algorithm is more robust than the existing deep learning models such as Attention U-Net and MUM-Net, and its tampering sensitivity remains at the same level as that of Attention U-Net and MUM-Net. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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20 pages, 6080 KiB  
Article
Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas
Remote Sens. 2020, 12(2), 243; https://doi.org/10.3390/rs12020243 - 10 Jan 2020
Cited by 15 | Viewed by 4114
Abstract
Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with the support of auxiliary datasets, using thresholding methods and [...] Read more.
Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with the support of auxiliary datasets, using thresholding methods and followed by morphological operations to further refine the results. However, auxiliary datasets may lose efficacy on large plain areas, whilst the parameters of morphological operations are hard to be decided in different situations. Here, a heuristic and automatic water extraction (HAWE) method is proposed to extract the water map from Sentinel-1 SAR data. In the HAWE, we integrate tile-based thresholding and the active contour model, in which the former provides a convincing initial water map used as a heuristic input, and the latter refines the initial map by using image gradient information. The proposed approach was tested on the Dongting Lake plain (China) by comparing the extracted water map with the reference data derived from the Sentinel-2 dataset. For the two selected test sites, the overall accuracy of water classification is between 94.90% and 97.21% whilst the Kappa coefficient is within the range of 0.89 and 0.94. For the entire study area, the overall accuracy is between 94.32% and 96.7% and the Kappa coefficient ranges from 0.80 to 0.90. The results show that the proposed method is capable of extracting water inundations with satisfying accuracy. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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29 pages, 3032 KiB  
Article
Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network
Remote Sens. 2019, 11(23), 2852; https://doi.org/10.3390/rs11232852 - 01 Dec 2019
Cited by 31 | Viewed by 3389
Abstract
Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. [...] Read more.
Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles’ scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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21 pages, 17588 KiB  
Article
Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data
Remote Sens. 2019, 11(21), 2586; https://doi.org/10.3390/rs11212586 - 04 Nov 2019
Cited by 54 | Viewed by 10645
Abstract
The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels [...] Read more.
The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels as processing units. However, the over-segmented images become non-Euclidean structure data that traditional deep Convolutional Neural Networks (CNN) cannot directly process. Here, we propose a novel Attention Graph Convolution Network (AGCN) to perform superpixel-wise segmentation in big SAR imagery data. AGCN consists of an attention mechanism layer and Graph Convolution Networks (GCN). GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification. The attention mechanism layer is introduced to guide the graph convolution layers to focus on the most relevant nodes in order to make decisions by specifying different coefficients to different nodes in a neighbourhood. The attention layer is located before the convolution layers, and noisy information from the neighbouring nodes has less negative influence on the attention coefficients. Quantified experiments on two airborne SAR image datasets prove that the proposed method outperforms the other state-of-the-art segmentation approaches. Its computation time is also far less than the current mainstream pixel-level semantic segmentation networks. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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21 pages, 2197 KiB  
Article
Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors
Remote Sens. 2019, 11(21), 2531; https://doi.org/10.3390/rs11212531 - 29 Oct 2019
Cited by 12 | Viewed by 3140
Abstract
Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone [...] Read more.
Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recognition framework based on the multi-task learning strategy, which can reduce computational demand, better exploit complementary information between the two recognition tasks, and lead to higher recognition performance. We also extend the joint recognition framework so that additional information, such as user identification with biometric motion analysis, can be offered. We evaluate our work systematically and comprehensively on two datasets with real-world settings. Our joint recognition model achieves the promising performance of 0.9174 in terms of F 1 -score for user identification on the benchmark RealWorld Human Activity Recognition (HAR) dataset. On the other hand, in comparison with the conventional approach, the proposed joint model is shown to be able to improve human activity recognition and position recognition by 5.1 % and 9.6 % respectively. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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17 pages, 14768 KiB  
Technical Note
MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing
Remote Sens. 2021, 13(16), 3275; https://doi.org/10.3390/rs13163275 - 19 Aug 2021
Cited by 12 | Viewed by 3343
Abstract
Scene understanding of satellite and aerial images is a pivotal task in various remote sensing (RS) practices, such as land cover and urban development monitoring. In recent years, neural networks have become a de-facto standard in many of these applications. However, semantic segmentation [...] Read more.
Scene understanding of satellite and aerial images is a pivotal task in various remote sensing (RS) practices, such as land cover and urban development monitoring. In recent years, neural networks have become a de-facto standard in many of these applications. However, semantic segmentation still remains a challenging task. With respect to other computer vision (CV) areas, in RS large labeled datasets are not very often available, due to their large cost and to the required manpower. On the other hand, self-supervised learning (SSL) is earning more and more interest in CV, reaching state-of-the-art in several tasks. In spite of this, most SSL models, pretrained on huge datasets like ImageNet, do not perform particularly well on RS data. For this reason, we propose a combination of a SSL algorithm (particularly, Online Bag of Words) and a semantic segmentation algorithm, shaped for aerial images (namely, Multistage Attention ResU-Net), to show new encouraging results (i.e., 81.76% mIoU with ResNet-18 backbone) on the ISPRS Vaihingen dataset. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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