A review on real-time big data analysis in remote sensing applications

Pekturk, Mustafa Kemal; Ünal, Muhammet

doi:10.1109/siu.2017.7960499

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…Specifically, the images obtained on May 12 and September 17, 2013, can provide high-quality optical data before and after the flood event, respectively. These images are publicly available from the United States Geological Survey (USGS) website 4 with a fine spatial resolution of 30 m. In addition, the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm was adopted for atmospheric correction of the Landsat 8 OLI images [121]. In this context, remote sensing features (capable of characterizing floods) can be calculated for subsequent data fusion.…”

Section: ) Remote Sensing Datamentioning

confidence: 99%

“…fact that the associated time constraints limit their applicability (especially, in the context of emergency response applications [4], [5]). Distributed computing, which has already been adopted in many remote sensing big data applications [6]- [8], is increasingly viewed as a feasible strategy to address the computational challenges brought by massive multisource data processing and fusion techniques [9]- [12].…”

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confidence: 99%

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Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Liu

Lei

et al. 2021

Proc. IEEE

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Despite the wide availability of remote sensing big data from numerous different Earth Observation (EO) instruments, the limitations in the spatial and temporal resolution of such EO sensors (as well as atmospheric opacity and other kinds of interferers) have led to many situations in which using only remote sensing data cannot fully meet the requirements of applications in which a (near) real-time response is needed. Examples of these applications include floods, earthquakes, and other kinds of natural disasters, such as typhoons. To address this issue, social media data have gradually been adopted to fill possible gaps in the analysis when remote sensing data are lacking or incomplete. In this case, the fusion of heterogeneous big data streams from multiple data sources introduces significant demands from a computational viewpoint. In order to meet these challenges,

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Section: ) Remote Sensing Datamentioning

confidence: 99%

mentioning

confidence: 99%

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Liu

Lei

et al. 2021

Proc. IEEE

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“…EO data apparently have the characteristics of time dimension [1,9], so the research on spatiotemporal DGGS framework is also an important direction. Near-real-time or real-time analyses of Earth observation data are also imminent or asked in applications [11,93]. Time has the same problem as space partition and space scale [94,95].…”

Section: Spatiotemporal Dggs Framework For Eo Datamentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.Remote Sens. 2020, 12, 62 2 of 15 (CEOS), over 500 EO satellites have been launched in the last half-century, and more than 150 satellites will be launched in the next 12 years [2,3]. China has launched more than 60 satellites since 1970 for comprehensive observation of the Earth's systems, including HuanJing (HJ), FengYun (FY), China-brazil earth resource satellite (CBERS), and GaoFen (GF) series. From the European Space Agency (ESA), in terms of Sentinel family, a total of 50,964,670 products had been downloaded by users since the start of data access operations, with a total data volume of 41. 35 PB [4]. The big Earth observation data (BEOD) has gradually promoted the development of global industries, research institutions, and application sectors, which has had a profound impact on the study of the Earth system [5,6], contributing to human activities, environmental monitoring, and climate changes, and also provided abundant data resource for the construction of digital Earth [7][8][9].BEOD also poses challenges to uses in terms of problem complexity, automatic analysis, and processing efficiency [10][11][12][13]. Fortunately, the rapid advancement of cloud computing technology in recent years provides strong computing power, especially for the efficiency of big geospatial data management and process...

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“…With the upcoming new model, data locality should be succeeded with recent technologies which contain tiling, data layout, array views, task and thread affinity, and topologyaware communication libraries. Combination of the best of these technologies can help us develop a comprehensive model for managing data locality on a high-performance computing system [49].…”

Section: Performance-aware Hpcmentioning

confidence: 99%

Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics

Pekturk¹,

Ünal²

2018

Data Mining

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The incredible increase in the volume of data emerging along with recent technological developments has made the analysis processes which use traditional approaches more difficult for many organizations. Especially applications involving subjects that require timely processing and big data such as satellite imagery, sensor data, bank operations, web servers, and social networks require efficient mechanisms for collecting, storing, processing, and analyzing these data. At this point, big data analytics, which contains data mining, machine learning, statistics, and similar techniques, comes to the help of organizations for end-to-end managing of the data. In this chapter, we introduce a novel high-performance computing system on the geo-distributed private cloud for remote sensing applications, which takes advantages of network topology, exploits utilization and workloads of CPU, storage, and memory resources in a distributed fashion, and optimizes resource allocation for realizing big data analytics efficiently.

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A review on real-time big data analysis in remote sensing applications

Cited by 13 publications

References 23 publications

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics

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