Call for papers: Special issue on deep learning and evolutionary computation for satellite imagery

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Mining and Analytics Pub Date : 2021-12-27 DOI:10.26599/BDMA.2021.9020025

引用次数: 0

Abstract

Satellite images are humungous sources of data that require efficient methods for knowledge discovery. The increased availability of earth data from satellite images has immense opportunities in various fields. However, the volume and heterogeneity of data poses serious computational challenges. The development of efficient techniques has the potential of discovering hidden information from these images. This knowledge can be used in various activities related to planning, monitoring, and managing the earth resources. Deep learning are being widely used for image analysis and processing. Deep learning based models can be effectively used for mining and knowledge discovery from satellite images.

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论文征集:卫星图像的深度学习和进化计算特刊

卫星图像是巨大的数据来源，需要有效的知识发现方法。卫星图像中地球数据的可用性增加在各个领域都有巨大的机会。然而，数据的数量和异构性带来了严重的计算挑战。高效技术的发展有可能从这些图像中发现隐藏的信息。这些知识可以用于与规划、监测和管理地球资源有关的各种活动。深度学习正被广泛用于图像分析和处理。基于深度学习的模型可以有效地用于从卫星图像中挖掘和发现知识。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.

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