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Remote Sensing
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Remote Sensing and IoT for Smart Learning Environments
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Remote Sensing and IoT for Smart Learning Environments

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 27554

Special Issue Editors

Dr. Priyan Malarvizhi Kumar

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Guest Editor
Computer Science and Engineering Department, Kyung Hee University, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea
Interests: Internet of Things; big data analytics; machine learning; health data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Hari Mohan Pandey

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Guest Editor
Department of Computer Science, Edge Hill University, Ormskirk, UK
Interests: artificial intelligence; soft computing techniques; natural language processing; language acquisition and machine learning algorithms
Prof. Dr. Gautam Srivastava

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Guest Editor
Department of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada
Interests: blockchain technology; cryptography; big data; data mining; social networks; security and privacy; anonymity; graphs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The educational industry has been continuously adapting to technological advancement since its inception. Access to digital space has increased huge opportunities for the learning world. Conservative models in learning have seen both success and failure over the years. Specialized approaches with personalized factors have become one of the important parts of the learning curve for students with the implementation of smart learning environment. There is a variety of smart learning cultures prevalent in the environment, as every institution or organization has their own approaches, procedures, techniques, and processes in the creation of their learning environment. Since all students have different abilities, inculcating smart learning methods may bring out hidden abilities. The failure in every learning system is the focus of human centered challenges. To overcome these challenges, more accurately advanced sensing technologies can be helpful. Identifying the various changes in the environment has become an easy way by using remote sensors

Advanced technologies like remote sensing and the Internet of Things (IoT) will be perfect for controlling various activities in a smart learning environment. Utilizing smart devices such as Smart Television, computing devices, mobile device apps, various tracking sensors, and many more in the learning environment can be used in the administering of smart learning. Also, integrating remote sensing and IoT can provide various opportunities for learners and teachers in conducting research and training to channel knowledge in schools, colleges, universities, or organizations. Moreover, enhancing educational innovations in physical spaces shall create a faster and more perfect learning curve. In addition, remote technologies can digitally connect academicians, teachers, counselors, and students in a single platform. Here, the consideration of implementing security and safety measures in these remote sensing technologies also acts as an added advantage of creating a quality learning environment. Currently, the smart learning environment is being equipped with the application of various sensing technologies like posture learning, emotion recognition, context-aware learning, augmented reality, ambient learning, etc. 

However, concentrating on the areas of planning, framework, protocol, and algorithm for smart sensing can increase the high performance of hardware platform or software framework. The main aim of this Special Issue is to invite scholars, academicians, and professionals to submit their research works, ideas, and implementations related to remote sensing and IoT platforms for Smart Learning Environments

The scope of this Special Issue includes but is not limited to the following:

  • Creating a ubiquitous learning environment using remote sensing and IoT
  • Sensor data processing and integration for Smart Learning Environments
  • Need of energy efficient protocols for remote sensing and IoT in smart environments
  • Efficient network protocols for smart objects based on communication platforms
  • A design-based thinking to adapt a framework for Smart Learning Environments
  • Best practices in implementation of smart learning environments
  • Need of smart sensing technologies for learning environments
  • Remote sensing and IoT based pedagogical tools for supporting smart learning
  • A study on policy-related issues for remote sensing and IoT based learning platforms
  • Architectural frameworks for IoT based Smart Learning Environments
  • Advanced sensor technologies used in learning environments: an overview
  • Autonomous sensor networks for smart learning environments

Dr. Priyan Malarvizhi Kumar
Dr. Hari Mohan Pandey
Dr. Gautam Srivastava
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.

Published Papers (4 papers)

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Research

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36 pages, 10418 KiB  
Article
Remote Sensing Imagery Segmentation: A Hybrid Approach
by Shreya Pare, Himanshu Mittal, Mohammad Sajid, Jagdish Chand Bansal, Amit Saxena, Tony Jan, Witold Pedrycz and Mukesh Prasad
Remote Sens. 2021, 13(22), 4604; https://doi.org/10.3390/rs13224604 - 16 Nov 2021
Cited by 6 | Viewed by 2216
Abstract
In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation [...] Read more.
In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity. Full article
(This article belongs to the Special Issue Remote Sensing and IoT for Smart Learning Environments)
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18 pages, 3708 KiB  
Article
Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection
by Chen Chen, Hongxiang Ma, Guorun Yao, Ning Lv, Hua Yang, Cong Li and Shaohua Wan
Remote Sens. 2021, 13(10), 1894; https://doi.org/10.3390/rs13101894 - 12 May 2021
Cited by 10 | Viewed by 2250
Abstract
Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become [...] Read more.
Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, “Inception Score”, “Human Rank”, and “Inference Time” are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model. Full article
(This article belongs to the Special Issue Remote Sensing and IoT for Smart Learning Environments)
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21 pages, 1232 KiB  
Article
Comparative Study of IoT-Based Topology Maintenance Protocol in a Wireless Sensor Network for Structural Health Monitoring
by Md. Ershadul Haque, Md. Asikuzzaman, Imran Ullah Khan, In-Ho Ra, Md. Sanwar Hossain and Syed Bilal Hussain Shah
Remote Sens. 2020, 12(15), 2358; https://doi.org/10.3390/rs12152358 - 23 Jul 2020
Cited by 32 | Viewed by 4941
Abstract
A structural health monitoring (SHM) system is an approach for identifying the damages caused to various kinds of structures using different system functions and providing the necessary feedback about structure’s conditions. As civil structures are the backbone of our society, to determine its [...] Read more.
A structural health monitoring (SHM) system is an approach for identifying the damages caused to various kinds of structures using different system functions and providing the necessary feedback about structure’s conditions. As civil structures are the backbone of our society, to determine its daily operations is a very important issue. The performance measurement of those structures is manual whereas a computer-based monitoring system could automatically assess the structural damages and identify its exact location. Recently, wireless sensor networks (WSNs) have attracted a great deal of attention for remote sensing applications due to flexibility to measure of various activity of large scale network. Since technology is advancing day by day, the overall cost of a monitoring system is also decreased. However, the major challenging fact of a WSNs is to provide scalability for covering a large area. The main question is arisen how much capable have of a monitoring system to turn off unnecessary nodes to save energy while there are no events detected. To support the scalability required of an existing network and save the node energy for future use, we propose a topology maintenance protocol integrated with construction to address the issue of a node’s energy consumption by placing it optimally and extending the monitoring system’s lifetime. As per the authors’ acknowledgement that, a little attention has been paid to developing such a hybrid approach. To mitigate node energy consumption issue with large scale support, an Internet of Things (IoT)-based maintenance approach is the best candidate for obtaining better system lifetime responses. Therefore, the main goal of this work is to develop an ‘on-the-fly’-based topology maintenance monitoring system, which can maintain a network’s infrastructure while gathering a node’s information to switch its state regularly when the present network is no longer optimal. Full article
(This article belongs to the Special Issue Remote Sensing and IoT for Smart Learning Environments)
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Review

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29 pages, 1522 KiB  
Review
Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities
by Sami Khanal, Kushal KC, John P. Fulton, Scott Shearer and Erdal Ozkan
Remote Sens. 2020, 12(22), 3783; https://doi.org/10.3390/rs12223783 - 19 Nov 2020
Cited by 130 | Viewed by 16815
Abstract
Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor [...] Read more.
Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor technologies, data management and data analytics, currently, several RS options are available to the agricultural community. However, the agricultural sector is yet to implement RS technologies fully due to knowledge gaps on their sufficiency, appropriateness and techno-economic feasibilities. This study reviewed the literature between 2000 to 2019 that focused on the application of RS technologies in production agriculture, ranging from field preparation, planting, and in-season applications to harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. We found an increasing trend in the use of RS technologies in agricultural production over the past 20 years, with a sharp increase in applications of unmanned aerial systems (UASs) after 2015. The largest number of scientific papers related to UASs originated from Europe (34%), followed by the United States (20%) and China (11%). Most of the prior RS studies have focused on soil moisture and in-season crop health monitoring, and less in areas such as soil compaction, subsurface drainage, and crop grain quality monitoring. In summary, the literature highlighted that RS technologies can be used to support site-specific management decisions at various stages of crop production, helping to optimize crop production while addressing environmental quality, profitability, and sustainability. Full article
(This article belongs to the Special Issue Remote Sensing and IoT for Smart Learning Environments)
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