Combining Mineral Sources and Environment through Deep Learning: A Synergy of Remote Sensing Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: closed (5 December 2023) | Viewed by 670
Special Issue Editors
Interests: wireless sensor networks; next generation networks; information security; IoT and FoG computing
2. Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
Interests: lightweight cryptography; elliptic/hyper elliptic curve cryptography; multimedia security; e-payment systems, MANETs; SIP authentication; smart grid security; IP multimedia subsystems and next generation networks
2. Big Data Research Center, Jeju National University, Jeju, Republic of Korea
Interests: applied machine learning; application of prediction; optimization algorithms for building IoT-based edge intelligence solutions; machine learning; distributed machine learning; continual learning; optimization of deep learning models
Special Issue Information
Dear Colleagues,
Remote sensing allows users to view the globe using several methods of information interpretation with the help of satellites. For the last few decades, this approach has figured prominently in resource evaluation and pollution monitoring by generating and analyzing multi-spectral images. Remote sensing is attracting increasing interest due to elevated products for various mineral resources, particularly those used in primary sectors such as sustainable power, and the significance of safeguarding our living environment from mining’s side effects. Deep learning (DL) methods of many types have traditionally been useful in environmental remote sensing investigations. With the growing amount of “big data” from planetary exploration and quick improvements in machine learning, new methods for Earth environmental monitoring have arisen. Mineral resources solutions are the most effective way to address major environmental issues.
DL possibilities in atmospheric remote sensing will be examined, spanning lithological classification, the collection of meteorological conditions, pattern matching and image compression, and knowledge rebuilding and prediction. Also of interest are neural-network-based learning models from the extraction of information, a set of task-specific characteristics, and the best system parameters for the underpinning learner. Despite the common constraints of this type of data, it offers essential autonomous strategic planning for remote sensing data. Presently, a significant decrease in precious resources necessitates the use of novel techniques to continuously improve message queues. On the other hand, new materials and applications necessitate increasingly higher sensitivity, selectivity, and improved application qualities. Mineral resources are important for various businesses, but responsible mineral development can only be achieved if it maximizes effective resource usage and minimizes harmful effects from extraction, treatment, and transportation. Furthermore, the production costs and operations’ environmental impact must be reduced.
The processing of remote sensing data has always presented challenging computation and communication difficulties in finding objects of interest, such as geothermal amendment and bottom ash, generated mainly by distortion and scant knowledge. In this Special Issue, “Mineral Processing for Advanced Material Applications”, Remote Sensing will aggregate current research and technological progress. Data with spatial and temporal relationships, for example, make machine-learning techniques difficult to apply and necessitate novel modeling methodologies that consider remote sensing data properties. This Special Issue will be an excellent venue for reporting completely redesigned information on mineral resources and their impact on the environment. We invite submissions exploring scientific advice from various disciplinary viewpoints, such as the merging of mineral resources and the environment through DL, as well as the resonance of remote sensing.
We welcome articles exploring topics including, but not limited to:
- Deep reinforcement learning applied to the distribution of resources in an unpredictable systems environment;
- In cognitive radio networks, machine-learning possibilities for managing resources;
- Decentralized utilization of resources using deep optimization techniques and applications;
- Evaluating natural resource depletion in the context of a sustainable future;
- Surveillance of high suspended materials using a combination of remote sensing and numerical modeling;
- Internet of Things (IoT) and artificial intelligence (AI) on the horizon for remote crop sensing;
- Remote-sensing-oriented data synergy for thermochemical conversion mineral resource assessment;
- Mineral resources analysis and geographic navigation software predicated on remote sensing documentation;
- The generation and threading of SAR (synthetic aperture radar) images for environmental remote sensing;
- Remote sensing techniques and applications for environmental investigation in the perspective of changing atmosphere;
- Wildfire tracking, spectroscopy, and remotely sensed data from SAR;
- Capturing commodities and understanding the environment with a remote-controlled ground vehicle;
- Remote sensing to identify pollution in urban regions.
Dr. Anwar Ghani
Dr. Shehzad Ashraf Chaudhry
Dr. Rashid Ahmad
Guest Editors
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