scholarly journals Analysis of the efficiency of Earth remote sensing means

2021 ◽  
Vol 2021 (4) ◽  
pp. 79-88
Author(s):  
P.P. Khorolskyi ◽  
◽  
V.T. Marchenko ◽  
N.P. Sazina ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Open Access ◽  
Data Processing ◽  
Data Use ◽  
State Budget ◽  
Earth Remote Sensing ◽  
Private Partnership ◽  
Private And Public

The aim of this paper is to analyze the efficiency of use of Earth remote sensing (ERS) means in the light of the trends in their development in the past ten years. The paper analyzes the efficiency of use of ERS means in the interests of socioeconomic development (in cartography, meteorology, climatology, oceanology, hydrology, agriculture, forestry, in local and regional management tasks, and in emergencies), the efficiency of the Indian ERS segment (as an example of one of the leading ERS countries), the basic trends in the development of ERS systems that increase their efficiency (open access to ERS data, private and public-private partnership, information delivery promptness, onboard ERS data processing, and ERS-based analysis), and a comparison of the ERS data market fraction of drones with that of satellites. As a result, the following global ERS trends that increase the efficiency of ERS data use are identified: - gradual reorientation from purely obtaining ERS data to making an analysis based thereon; - intensive development of methods of geospatial monitoring, business analysis, machine learning, neural networks, cloud architecture, and automatic processing of large ERS data arrays; - despite the ample scope for ERS data use and the reduction of space imagery prices, this information, as estimated by some analysts, is used in the solution of socioeconomic problems only to quite a small extent because less than one per cent of the ERS satellite data can ever find their users; - in India, China, the Russian Federation, and Ukraine, ERS is funded from the state budget, which is no longer the case in most of the developed countries, where public-private and commercial ERS structures are dominant; - in the countries where ERS is mostly funded from the state budget, the approach to the distribution of ERS products on the home market with the aim to compensate for the capital costs of ERS satellite development inevitably produces negative results; - the formation of national ERS data markets is in progress; the features of these markets are open access to ERS data, private and public-private partnership, information delivery promptitude due to the use of web servers and cloud computing, ERS-based analysis, and onboard ERS data processing in the near future; - in the long term, the future of ERS will depend on breakthrough technologies, innovative solutions, new applications, and the integration of technologies such as VR (virtual reality), AR (added reality), AI (artificial intelligence), Ml (machine learning), Big Data, Cloud Computing, and IoT (Internet of things), which will be of crucial importance in the ERS segment. In the paper, the system analysis method is used. The practical significance of the paper lies in the possibility of using the global ERS advancement trends in the development and operation of national ERS spacecraft.

scholarly journals Cloud Computing Cloud Computing in Remote Sensing : High Performance Remote Sensing Data Processing in a Big data Environment

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Yassine Sabri ◽  
Aouad Siham
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Data Processing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Remote Sensing Data ◽  
Cloud Service ◽  
Intermediate Data ◽  
High Level

Multi-area and multi-faceted remote sensing (SAR) datasets are widely used due to the increasing demand for accurate and up-to-date information on resources and the environment for regional and global monitoring. In general, the processing of RS data involves a complex multi-step processing sequence that includes several independent processing steps depending on the type of RS application. The processing of RS data for regional disaster and environmental monitoring is recognized as computationally and data demanding.Recently, by combining cloud computing and HPC technology, we propose a method to efficiently solve these problems by searching for a large-scale RS data processing system suitable for various applications. Real-time on-demand service. The ubiquitous, elastic, and high-level transparency of the cloud computing model makes it possible to run massive RS data management and data processing monitoring dynamic environments in any cloud. via the web interface. Hilbert-based data indexing methods are used to optimally query and access RS images, RS data products, and intermediate data. The core of the cloud service provides a parallel file system of large RS data and an interface for accessing RS data from time to time to improve localization of the data. It collects data and optimizes I/O performance. Our experimental analysis demonstrated the effectiveness of our method platform.

Machine Learning and Cloud Computing for Remote Monitoring of Wave Piercing Catamarans: A Case Study using MATLAB on Amazon Web Services

2020 ◽  
pp. 83-94
Author(s):  
Babak Shabani ◽  
Jason Ali-Lavroff ◽  
Damien Holloway ◽  
Spiridon Penev ◽  
Daniele Dessi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Web Services ◽  
Data Processing ◽  
High Speed ◽  
Short Term Memory ◽  
Wave Load ◽  
Efficient Manner ◽  
Amazon Web Services

Wave load cycles, wet-deck slamming events, accelerations and motion comfort are important considerations for high- speed catamarans operating in moderate to large waves. This paper provides an overview of data analytics methods and cloud computing resources for remotely monitoring motions and structural responses of a 111 m high-speed catamaran. To satisfy the data processing requirements, MATLAB Reference Architectures on Amazon Web Services (AWS) were used. Such combination enabled fast parallel computing and advanced feature engineering in a time-efficient manner. A MATLAB Production Server on AWS has been set up for near real-time analytics and execution of functions developed according to the class guidelines. A case study using Long Short-Term Memory (LSTM) networks for ship speed and Motion Sickness Incidence (MSI) is provided and discussed. Such data architecture provides a flexible and scalable solution, leading to deeper insights through big data processing and machine learning, which supports hull monitoring functions as a service.

Security Issues in Fog Computing and ML-Based Solutions

2021 ◽  
pp. 209-234
Author(s):  
Himanshu Sahu ◽  
Gaytri
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Processing ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Special Focus ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Security Issues ◽  
Learning Techniques

IoT requires data processing, which is provided by the cloud and fog computing. Fog computing shifts centralized data processing from the cloud data center to the edge, thereby supporting faster response due to reduced communication latencies. Its distributed architecture raises security and privacy issues; some are inherited from the cloud, IoT, and network whereas others are unique. Securing fog computing is equally important as securing cloud computing and IoT infrastructure. Security solutions used for cloud computing and IoT are similar but are not directly applicable in fog scenarios. Machine learning techniques are useful in security such as anomaly detection, intrusion detection, etc. So, to provide a systematic study, the chapter will cover fog computing architecture, parallel technologies, security requirements attacks, and security solutions with a special focus on machine learning techniques.

scholarly journals Aflatoxin detection on direction of the 4.0 age at 3.0 costs

2019 ◽  
Vol 7 (7) ◽  
pp. 338-346 ◽  
Author(s):  
Mariana Matulovic ◽  
Flávio José de Oliveira Morais ◽  
Angela Vacaro de Souza ◽  
Cleber Aalexandre de Amorim ◽  
Luiz Fernando Sommaggio Coletta
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Environmental Variables ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Toxic Metabolite ◽  
Monitoring And Control ◽  
Carcinogenic Effects ◽  
And Control

Articulate the most diverse and sophisticated technologies, such as Remote Sensing, Big Data, Cloud Computing, Internet of Things, 3D Printing, among others, is part of universe 4.0, whether industrial or agricultural. Focusing on agricultural context, this paper proposes a low-cost 4.0 device to perform the monitoring and control of certain environmental variables for the detection of aflatoxins in peanut crops. Aflatoxins are toxic metabolite of fungi genus Aspergillus that can cause toxic and carcinogenic effects in humans and animals. The device developed was able to monitor temperature and humidity variations helping the aflatoxins identification. The equipment portability allows its use in silos with encapsulation via Additive Manufacturing, besides the aflatoxin prediction from Machine Learning algorithms.

scholarly journals MAPPING GLACIER CHANGES USING CLUSTERING TECHNIQUES ON CLOUD COMPUTING INFRASTRUCTURE

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 29-34
Author(s):  
V. Ayma ◽  
C. Beltrán ◽  
P. N. Happ ◽  
G. A. O. P. Costa ◽  
R. Q. Feitosa
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Local Mode ◽  
Ground Truth Data ◽  
Computational Performance ◽  
Thematic Accuracy ◽  
Computing Infrastructure

<p><strong>Abstract.</strong> Climate change and its effects are taking more importance nowadays; and glaciers are one of the most affected ecosystems by that, considering that the energy of Earth’s surface and its temperature may be directly related to glacier temporal changes. Then, the comprehension of glaciers behaviour, by its retreating or melting critical conditions, can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by satellites sensors, we can refer to this analysis as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means and Expectation Maximization algorithms, as distributed clustering solutions, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral image analysis. In this work we extended the functionalities of that package, by making it able to process multispectral images using the aforementioned clustering algorithms. To validate our proposal, we analysed the Ausangate glacier, located on the Andes Mountains, in Peru, by mapping the changes in such environment through a multi-temporal Remote Sensing analysis. Our results and conclusions are focused on the thematic accuracy and the computational performance achieved by our proposed solution. Thematic accuracy was assessed by comparing the automatically detected glacier areas by the clustering approaches against the manually selected ground truth data. We compared the computational load involved in executing the clustering processes sequentially and in a distributed fashion, using a local mode and cluster configuration over a cloud computing infrastructure.</p>

scholarly journals Development of a Server for the Implementation of Data Processing Pipelines and ANN Training

2021 ◽  
Vol 7 (1) ◽  
pp. 38
Author(s):  
Brais Galdo ◽  
Daniel Rivero ◽  
Enrique Fernandez-Blanco
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Processing ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Enormous Amount

Data processing and the use of machine learning techniques make it possible to solve a wide variety of problems. The great disadvantage of using this type of technology is the enormous amount of computation involved. This is why we have tried to develop an architecture that makes the best possible use of the resources available on each machine. The growth of cloud computing and the rise of virtualization techniques have led to a development that allows these tasks to be carried out in a more optimized way.

scholarly journals Framework of cloud web services for processing remote sensing data

2019 ◽  
Vol 75 ◽  
pp. 03001
Author(s):  
Alexey Buchnev ◽  
Pavel Kim ◽  
Valery Pyatkin ◽  
Fedor Pyatkin ◽  
Evgeny Rusin
Keyword(s):  
Remote Sensing ◽  
Web Services ◽  
Data Processing ◽  
Processing System ◽  
Remote Sensing Data ◽  
Functional Modules ◽  
Distributed Network ◽  
Data Processing System ◽  
Earth Remote Sensing ◽  
Sensing Data

We consider a distributed network of cloud web services for processing satellite data, which provides data processing facilities for Earth remote sensing within SaaS model. In fact, this is a set of web services that implement the functional modules of the PlanetaMonitoring remote sensing data processing system.

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