Abstract:In recent years, the rapid development of Earth observation technology has produced an increasing growth in remote sensing big data, posing serious challenges for effective and efficient processing and analysis. Meanwhile, there has been a massive rise in deeplearning-based algorithms for remote sensing tasks, providing a large opportunity for remote sensing big data. In this article, we initially summarize the features of remote sensing big data. Subsequently, following the pipeline of remote sensing tasks, a… Show more
“… As noted in Section 4.3.1 , the lack of large benchmark datasets is a bottleneck in water body detection and water quality monitoring research utilizing RS imagery and AI. The dominant methods in both water domains are supervised learning, which often requires very large, labeled datasets to train on, thus, there is a clear, urgent need for semi-supervised and unsupervised learning methods [ 15 ]. Unsupervised learning methods are able to learn from big sets of unlabeled data, as demonstrated in [ 29 , 46 ].…”
Section: Challenges and Opportunitiesmentioning
confidence: 99%
“… Most current ML/DL-based RS research focuses on borrowing or slightly improving ML/DL/CV models from computer science [ 79 , 120 ]. Compared with natural scene images, RS data are multiresolution, multitemporal, multispectral, multiview, and multitarget [ 15 ]. Slight modifications of ML/DL/CV models simply cannot cope with the special challenges posed in RS big data.…”
Section: Challenges and Opportunitiesmentioning
confidence: 99%
“…Slight modifications of ML/DL/CV models simply cannot cope with the special challenges posed in RS big data. New ML/DL models specialized for RS big data are thus urgently needed [ 15 , 18 ]. We hope our review will draw the attention of researchers who have a multidisciplinary background to this issue.…”
Section: Challenges and Opportunitiesmentioning
confidence: 99%
“…We hope our review will draw the attention of researchers who have a multidisciplinary background to this issue. Looking deep into the mechanisms of RS and land surface processes, studying the characteristics of RS imagery would guide the design of specialized ML/DL models for RS big data and thus further improve RS applications using AI in breadth and depth [ 15 ].…”
Section: Challenges and Opportunitiesmentioning
confidence: 99%
“…Remote sensing (RS) is the single largest source of geospatial big data and has increased dramatically in terms of both spatial and temporal resolution. This poses serious challenges for effective and efficient processing and analysis [ 15 ]. Meanwhile, recent advances in DL and CV have significantly improved research in RS and geosciences [ 16 , 17 , 18 ].…”
Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.
“… As noted in Section 4.3.1 , the lack of large benchmark datasets is a bottleneck in water body detection and water quality monitoring research utilizing RS imagery and AI. The dominant methods in both water domains are supervised learning, which often requires very large, labeled datasets to train on, thus, there is a clear, urgent need for semi-supervised and unsupervised learning methods [ 15 ]. Unsupervised learning methods are able to learn from big sets of unlabeled data, as demonstrated in [ 29 , 46 ].…”
Section: Challenges and Opportunitiesmentioning
confidence: 99%
“… Most current ML/DL-based RS research focuses on borrowing or slightly improving ML/DL/CV models from computer science [ 79 , 120 ]. Compared with natural scene images, RS data are multiresolution, multitemporal, multispectral, multiview, and multitarget [ 15 ]. Slight modifications of ML/DL/CV models simply cannot cope with the special challenges posed in RS big data.…”
Section: Challenges and Opportunitiesmentioning
confidence: 99%
“…Slight modifications of ML/DL/CV models simply cannot cope with the special challenges posed in RS big data. New ML/DL models specialized for RS big data are thus urgently needed [ 15 , 18 ]. We hope our review will draw the attention of researchers who have a multidisciplinary background to this issue.…”
Section: Challenges and Opportunitiesmentioning
confidence: 99%
“…We hope our review will draw the attention of researchers who have a multidisciplinary background to this issue. Looking deep into the mechanisms of RS and land surface processes, studying the characteristics of RS imagery would guide the design of specialized ML/DL models for RS big data and thus further improve RS applications using AI in breadth and depth [ 15 ].…”
Section: Challenges and Opportunitiesmentioning
confidence: 99%
“…Remote sensing (RS) is the single largest source of geospatial big data and has increased dramatically in terms of both spatial and temporal resolution. This poses serious challenges for effective and efficient processing and analysis [ 15 ]. Meanwhile, recent advances in DL and CV have significantly improved research in RS and geosciences [ 16 , 17 , 18 ].…”
Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.
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