Deep learning in environmental remote sensing: Achievements and challenges

By Qiangqiang Yuan, Huanfeng Shen, Tongwen Li, Zhiwei Li, Shuwen Li, Yun Jiang, Hongzhang Xu, Weiwei Tan, Qianqian Yang, Jiwen Wang and 2 more. Remote Sensing of Environment, 2020

1,475 citations recorded by the source corpus.

Abstract Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of “big data” from earth observation and rapid advances in ML, increasing opportunities for novel methods have emerged to aid in earth environmental monitoring. Over the last decade, a typical and state-of-the-art ML framework named deep learning (DL), which is developed from the traditional neural network (NN), has outperformed traditional models with considerable improvement in performance. Substantial progress in developing a DL methodology for a variety of earth science applications has been observed. Therefore, this review will concentrate on the use of the traditional NN and DL methods to advance the environmental remote sensing process. First, the potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed. A typical network structure will then be introduced. Afterward, the applications of DL environmental monitoring in the atmosphere, vegetation, hydrology, air and land surface temperature, evapotranspiration, solar radiation, and ocean color are specifically reviewed. Finally, challenges and future perspectives will be comprehensively analyzed and discussed.

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/1e03bdf8b86d2969fff11277f9e3dcbfac132e28

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Citations in the corpus

Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images (2021, background): “tation technology plays an increasingly important role in global sustainable development, its need for a large number of globalwise, high-quality labeled samples is growing [25], [26].”
Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks (2023, background): “Over the past decade, deep learning (DL) has been garnering increasing attention in many application fields [1], owing to its powerful ability for data representation and learning.”
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, neutral): “, hierarchical representation of the dataset) in the model [191,192].”
Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works (2022, background): “OBTAINING a feature representation being both discriminative and invariable is fundamental in remote sensing image (RSI) understanding [1], [2], [3].”
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot (2023, background): “Remote sensing image analysis is an essential tool in various applications, including environmental monitoring, disaster management, urban planning, and many others [12, 57].”
Deep learning for change detection in remote sensing: a review (2022, background): “…existing studies reviewing deep learning are general reviews concerning the algorithm development of deep learning and remote sensing specific applications (Zhang, Zhang, and Du 2016; LeCun, Bengio, and Hinton 2015; Ball, Anderson, and Chan 2018; Ma et al. 2019a; Yuan et al. 2020; Zhu et al. 2017).”
A Review on Deep Learning in UAV Remote Sensing (2021, background): “In more of a “niche” type of research, environmental applications and land surface change detection were investigated in literature revision papers by Yuan et al. [201] and Khelifi et al. [106], respectively.”