Iterative integration of deep learning in hybrid Earth surface system modelling

Chen, Min; Qian, Zhen; Boers, Niklas; Jakeman, Anthony; Kettner, Albert J.; Brandt, Martin; Kwan, Mei‐Po; Batty, Michael; Li, Wenwen; Zhu, Rui; Luo, W.; Ames, Daniel P.; Barton, C. Michael; Cuddy, Susan M.; Koirala, Sujan; Zhang, Fan; Ratti, Carlo; Liu, Jian; Zhong, Teng; Liu, Junzhi; Wen, Yongning; Yue, Songshan; Zhu, Zhiyi; Zhang, Zhixin; Sun, Zhuo; Lin, Jian; Ma, Zaiyang; He, Yuanqing; Xu, Kai; Zhang, Chunxiao; Lin, Hui; Lv, Guonian

doi:10.1038/s43017-023-00452-7

Cited by 41 publications

(5 citation statements)

References 157 publications

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“…It is comparable to the idea of "black box" testing, which does not require the artificial selection of input features from the input to the output of a model. Deep learning is a high-quality data and data-driven approach [37,38].…”

Section: Digital Terrain Analysis Methodsmentioning

confidence: 99%

Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning

et al. 2023

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Detecting impact craters on the Martian surface is a critical component of studying Martian geomorphology and planetary evolution. Accurately determining impact crater boundaries, which are distinguishable geomorphic units, is important work in geological and geomorphological mapping. The Martian topography is more complex than that of the Moon, making the accurate detection of impact crater boundaries challenging. Currently, most techniques concentrate on replacing impact craters with circles or points. Accurate boundaries are more challenging to identify than simple circles. Therefore, a boundary delineator for Martian crater instances (BDMCI) using fusion data is proposed. First, the optical image, digital elevation model (DEM), and slope of elevation difference after filling the DEM (called slope of EL_Diff to highlight the boundaries of craters) were used in combination. Second, a benchmark dataset with annotations for accurate impact crater boundaries was created, and sample regions were chosen using prior geospatial knowledge and an optimization strategy for the proposed BDMCI framework. Third, the multiple models were fused to train at various scales using deep learning. To repair patch junction fractures, several postprocessing methods were devised. The proposed BDMCI framework was also used to expand the catalog of Martian impact craters between 65°S and 65°N. This study provides a reference for identifying terrain features and demonstrates the potential of deep learning algorithms in planetary science research.

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Section: Digital Terrain Analysis Methodsmentioning

confidence: 99%

Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning

et al. 2023

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“…Existing research has demonstrated that the process understanding and hybrid modeling of complex geographic space urgently requires the coupling of physical mechanisms and geographical knowledge. 1,2 Although the new RS technologies with three-dimensional observation, real-time perception, and spatiotemporal synergy have been able to realize all-around fine observation of the earth's surface environment, 3 precise understanding and functional perspectives of geographical space still lack a unified architecture. In addition, traditional RS interpretation units of pixels and objects are difficult to correspond to the real geographic entities with geographic attributes, which exacerbates the spatial and temporal structural differences between the geospatial RS interpretation and the geographic cognition of human beings.…”

Section: Architecture Of the Cognitive Rsmentioning

confidence: 99%

Geospatial remote sensing interpretation: From perception to cognition

Wang,

Han,

Zhang

et al. 2024

TIG

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“…On the one hand, the effect of machine learning depends on feature selection (Guyon and Elisseeff, 2003;Domingos, 2012), which requires a high level of expertise for the user. Deep learning is data-driven with the ability of automatic representation learning (Qian et al, 2022;Chen et al, 2023). On the other hand, traditional machine learning provides interpretable models, while deep learning is often seen as a "black box".…”

Section: Identification Of Typical Geomorphic Units Of Marsmentioning

confidence: 99%

Progress and prospects for research on Martian topographic features and typical landform identification

Liu,

Cheng

2023

Front. Astron. Space Sci.

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The study of Martian surface topography is important for understanding the geological evolution of Mars and revealing the spatial differentiation of the Martian landscape. Identifying typical landform units is a fundamental task when studying the origin and evolution of Mars and provides important information for landing on and exploring Mars, as well as estimating the age of the Martian surface and inferring the evolution of the Earth’s environment. In this paper, we first investigate Mars exploration, data acquisition and mapping, and the classification methods of Martian landforms. Then, the identification of several typical Martian landform types, such as aeolian landforms, fluvial landforms, and impact landforms, is shown in detail. Finally, the prospects of Mars data acquisition, landform mapping, and the construction and identification of the Martian landform classification system are presented. The construction of the Martian landform classification system and the identification of typical Martian landforms using deep learning are important development directions in planetary science.

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Iterative integration of deep learning in hybrid Earth surface system modelling

Cited by 41 publications

References 157 publications

Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning

Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning

Geospatial remote sensing interpretation: From perception to cognition

Progress and prospects for research on Martian topographic features and typical landform identification

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