State of the Art: High-Performance and High-Throughput Computing for Remote Sensing Big Data

Zhang, Sheng; Xue, Yong; Zhou, Xingxing; Zhang, Xiaopeng; Liu, Wenhao; Li, Kaiyuan; Liu, Runze

doi:10.1109/mgrs.2022.3204590

Cited by 5 publications

(2 citation statements)

References 229 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Market-driven advanced computing systems have strategically shifted from HPC to High Throughput Computing (HTC). This transition aims to enhance not only processing speed but also to address critical issues like cost efficiency, energy savings, system reliability, and security [2]. Nonetheless, the escalating trend of data sharing across networks makes it imperative to employ programming models for executing programs across multiple distributed infrastructures.…”

Section: Introductionmentioning

confidence: 99%

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

Walia,

Kumar,

Gill

2024

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to a prodigious amount of data requiring everincreasing computations and services from cloud to the edge of the network. Fog/Edge computing is a promising and distributed computing paradigm that has drawn extensive attention from both industry and academia. The infrastructural efficiency of these computing paradigms necessitates adaptive resource management mechanisms for offloading decisions and efficient scheduling. Resource Management (RM) is a non-trivial issue whose complexity is the result of heterogeneous resources, incoming transactional workload, edge node discovery, and Quality of Service (QoS) parameters at the same time, which makes the efficacy of resources even more challenging. Hence, the researchers have adopted Artificial Intelligence (AI)-based techniques to resolve the abovementioned issues. This paper offers a comprehensive review of resource management issues and challenges in Fog/Edge paradigm by categorizing them into provisioning of computing resources, task offloading, resource scheduling, service placement, and load balancing. In addition, existing AI and non-AI based state-of-the-art solutions have been discussed, along with their QoS metrics, datasets analysed, limitations and challenges. The survey provides mathematical formulation corresponding to each categorized resource management issue. Our work sheds light on promising research directions on cutting-edge technologies such as Serverless computing, 5G, Industrial IoT (IIoT), blockchain, digital twins, quantum computing, and Software-Defined Networking (SDN), which can be integrated with the existing frameworks of fog/edge-of-things paradigms to improve business intelligence and analytics amongst IoT-based applications.

show abstract

Section: Introductionmentioning

confidence: 99%

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

Walia,

Kumar,

Gill

2024

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…Remote sensing images (RSI) are the key archives of human Earth observation, providing important survey proofs about the environment, agriculture, economy and so on. As the onboard sensor blooms, the rapid expansion of monitoring data has made a computer algorithm indispensable for RSI recognition (Chen et al, 2023;Kochupillai et al, 2022;Zhang et al, 2022a;Wijata et al, 2023). With the breakthroughs in machine learning, deep learning (DL) methods have dominated the recognition tasks of RSI, resulting in a lot of applications based on DL.…”

Section: Introductionmentioning

confidence: 99%

MBC-Net: long-range enhanced feature fusion for classifying remote sensing images

Song

2023

IJICC

View full text Add to dashboard Cite

PurposeClassification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.Design/methodology/approachThis work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.FindingsExtensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.Originality/valueMBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

show abstract

Efficient distributed matrix for resolving computational intensity in remote sensing

Zou,

Li,

Wang

et al. 2025

Future Generation Computer Systems

View full text Add to dashboard Cite

State of the Art: High-Performance and High-Throughput Computing for Remote Sensing Big Data

Cited by 5 publications

References 229 publications

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

MBC-Net: long-range enhanced feature fusion for classifying remote sensing images

Efficient distributed matrix for resolving computational intensity in remote sensing

Contact Info

Product

Resources

About