scholarly journals A BLOCKCHAIN-BASED APPROACH TO ENABLE REMOTE SENSING TRUSTED DATA

Author(s):  
M. Pincheira ◽  
E. Donini ◽  
R. Giaffreda ◽  
M. Vecchio

Abstract. Remote sensing considerably benefits from the fusion of open data from different sources, including far-range sensors mounted on satellites and short-range sensors on drones or Internet of Things devices. Open data is an emerging philosophy attracting an increasing number of data owners willing to share. However, most of the data owners are unknown and thus, untrustable, which makes shared data likely unreliable and possibly compromising associated outcomes. Currently, there exist tools that distribute open data, acting as intermediaries connecting data owners and users. However, these tools are managed by central authorities that set rules for data ownership, access, and integrity, limiting data owners and users. Therefore, a need emerges for a decentralized system to share and retrieve data without intermediaries limiting participants. Here, we propose a blockchain-based system to share and retrieve data without the need for a central authority. The proposed architecture (i) allows sharing data, (ii) maintains the data history (origin and updates), and (iii) allows retrieving and evaluating the data adding trustworthiness. To this end, the blockchain network enables the direct connection of data owners and users. Furthermore, blockchain automatically interacts with participants and keeps a transparent record of their actions. Hence, blockchain provides a decentralized database that enables trust among the participants without a central authority. We analyzed the potentials and critical issues of the architecture in a remote sensing use case of precision farming. The analysis shows that participants benefit from the properties of the blockchain in providing trusted data for remote sensing applications.


2019 ◽  
Vol 11 (24) ◽  
pp. 3056 ◽  
Author(s):  
Rocco Sedona ◽  
Gabriele Cavallaro ◽  
Jenia Jitsev ◽  
Alexandre Strube ◽  
Morris Riedel ◽  
...  

High-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of Remote Sensing (RS) data. This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines containing a large number of Graphics Processing Units (GPUs). The experimental results confirm that distributed training can drastically reduce the amount of time needed to perform full training, resulting in near linear scaling without loss of test accuracy.



2021 ◽  
Vol 13 (3) ◽  
pp. 366
Author(s):  
Renato Macciotta ◽  
Michael T. Hendry

Transportation infrastructure in mountainous terrain and through river valleys is exposed to a variety of landslide phenomena. This is particularly the case for highway and railway corridors in Western Canada that connect towns and industries through prairie valleys and the Canadian cordillera. The fluidity of these corridors is important for the economy of the country and the safety of workers, and users of this infrastructure is paramount. Stabilization of all active slopes is financially challenging given the extensive area where landslides are a possibility, and monitoring and minimization of slope failure consequences becomes an attractive risk management strategy. In this regard, remote sensing techniques provide a means for enhancing the monitoring toolbox of the geotechnical engineer. This includes an improved identification of active landslides in large areas, robust complement to in-place instrumentation for enhanced landslide investigation, and an improved definition of landslide extents and deformation mechanisms. This paper builds upon the extensive literature on the application of remote sensing techniques and discusses practical insights gained from a suite of case studies from the authors’ experience in Western Canada. The review of the case studies presents a variety of landslide mechanisms and remote sensing technologies. The aim of the paper is to transfer some of the insights gained through these case studies to the reader.



2021 ◽  
Vol 3 ◽  
pp. 100019
Author(s):  
Alvarez-Vanhard Emilien ◽  
Corpetti Thomas ◽  
Houet Thomas


Author(s):  
J.J. van der Sanden ◽  
P. Budkewitsch ◽  
D.G. Flett ◽  
A.L. Gray ◽  
R.K. Hawkins ◽  
...  




1986 ◽  
Vol 1 (4) ◽  
pp. 3-15 ◽  
Author(s):  
Deborah A. Kuchler ◽  
David L.B. Jupp ◽  
Daniel B. van R. Claasen ◽  
William Bour


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