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A New International Initiative for Facilitating Data-Driven Earth Science Transformation

Qiuming Cheng, Molei Zhao
2020 Geological Society Special Publication  
While recent monitoring data will help risk and resource assessment, the long-earth record is fundamental for understanding processes.  ...  Data-driven techniques including machine-learning (ML) algorithms with big data are re-activating and re-empowering research in traditional disciplines for solving new problems.  ...  Acknowledgements The senior author thanks the IUGS EC for their comments and support during the development of the big science program initiative.  ... 
doi:10.1144/sp499-2019-158 fatcat:bocgxsleyfbmvjhre3tkgtb4uq

Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean

Polina Lemenkova
2021 Zenodo  
Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean  ...  the extremely big Earth datasets Deep Learning can present the next step in processing big Earth data Deep learning is a new, rapidly evolving field of machine learning and consists in advanced algorithms  ...  ] , [47] Importance: The value of big Earth data processing to geoscience research: development of data-driven science [30] Introduction Big Spatial Data as an Opportunity Possibilities: Technical  ... 
doi:10.5281/zenodo.4785392 fatcat:efx3s47tebgrbfkcp5gtb7thqi

HESS Opinions: Deep learning as a promising avenue toward knowledge discovery in water sciences

Chaopeng Shen, Eric Laloy, Adrian Albert, Fi-John Chang, Amin Elshorbagy, Sangram Ganguly, Kuo-lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li (+1 others)
2018 Hydrology and Earth System Sciences Discussions  
, stimulate advances in machine learning; and (5) An urgent need for research is hydrology-customized methods for interpreting knowledge extracted by deep learning.  ...  </strong> Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model  ...  In the data-driven avenue, deep-learning models are created to learn from data to model a general, humandirected target.  ... 
doi:10.5194/hess-2018-168 fatcat:j3kx34w635ashnh2uyiulckesq

Overview on the Development of Intelligent Methods for Mineral Resource Prediction under the Background of Geological Big Data

Shi Li, Jianping Chen, Chang Liu
2022 Minerals  
This review briefly summarizes the research development of textual data mining and spatial data mining.  ...  However, many challenges have come forward, including four aspects: (i) discovery of prospecting big data based on geological knowledge system; (ii) construction of the conceptual prospecting model by  ...  one-stop processing analysis platform for earth science, solving major scientific problems of earth science driven by big data, and forming a deep communication integration platform of the digital earth  ... 
doi:10.3390/min12050616 fatcat:ztklcd3xsnamlefhiqf4ehr2xe

Machine Learning for Earth System Observation and Prediction

Massimo Bonavita, Rossella Arcucci, Alberto Carrassi, Peter Dueben, Alan J. Geer, Bertrand Le Saux, Nicolas Longépé, Pierre-Philippe Mathieu, Laure Raynaud
2020 Bulletin of The American Meteorological Society - (BAMS)  
We would like to express our appreciation to ECMWF Events Manager Karen Clarke for her impeccable organization of the logistics of the virtual Workshop and its successful delivery.  ...  Machine learning is generally purely data driven, it does not rely on any prior knowledge of the underlying process and is not exclusively applied to time-evolving systems.  ...  Machine learning for product development and ensemble processing.  ... 
doi:10.1175/bams-d-20-0307.1 fatcat:ddotbxuisnflzc3absqcrtlhq4

Uncertainty as a Driving Force for Geoscientific Development

Hendrik Paasche, Katja Paasche, Peter Dietrich
2020 Nature and Culture  
Geoscientists invest significant effort to cope with uncertainty in Earth system observation and modeling.  ...  Following a discovery science methodology results in greater potential for the quantification of uncertainty associated to scientific findings than staying inside hypothesis-driven science methodology,  ...  Methodologies can range from simple regression analysis to sophisticated data analytics, for example, deep learning algorithms with low suitability for cognitive understanding of the learned linkage function  ... 
doi:10.3167/nc.2020.150101 fatcat:azmafsidxzh7zby4bs4vspeyee

Machine Learning for the Geosciences: Challenges and Opportunities [article]

Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, and Vipin Kumar
2017 arXiv   pre-print
learning and geosciences for synergistic advancements in both disciplines.  ...  This article introduces researchers in the machine learning (ML) community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences  ...  Climate Change: A Data-driven Approach" (Award #1029711), the NSF-funded 2015 IS-GEO workshop (Award #1533930), and subsequent Research Collaboration Network (EarthCube RCN IS-GEO: Intelligent Systems  ... 
arXiv:1711.04708v1 fatcat:i7yjfka55fbn5biqcw7g2lafsa

Earth Science Deep Learning: Applications and Lessons Learned

Manil Maskey, Rahul Ramachandran, J.J. Miller, Jia Zhang, Iksha Gurung
2018 IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium  
At the NASA Marshall Space Flight Center (MSFC), the Data Science and Informatics Group (DSIG) has been using deep learning for a variety of Earth science applications.  ...  Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing.  ...  In medical science, deep learning is used for diagnosis and language translation. Self-driving cars are the latest advancements driven by deep learning.  ... 
doi:10.1109/igarss.2018.8517346 dblp:conf/igarss/MaskeyRMZG18 fatcat:ldqy5ybzofbxxbwz7f5nqpya4a

The Rise of AI-Driven Simulators: Building a New Crystal Ball [article]

Ian Foster, David Parkes, Stephan Zheng
2020 arXiv   pre-print
These advances may lead to a new era in computational simulation, in which sensors of many kinds are used to produce vast quantities of data, AI methods identify patterns in those data, and new AI-driven  ...  What if we could predict weather two weeks out, guide the design of new drugs for new viral diseases, or manage new manufacturing processes that cut production costs and times by an order of magnitude?  ...  Beroza, Machine learning for data-driven discovery in solid Earth geoscience.  ... 
arXiv:2012.06049v1 fatcat:su5pxwiim5aszfij55lylk3vxa

To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI [article]

Jingan Yang, Yang Peng
2020 arXiv   pre-print
science, neuroscience, cognitive science, psychology and data science; (ii) how is the electrical signal transmitted by the human brain?  ...  We believe that the frontier theory innovation of AI, knowledge-driven modeling methodologies for commonsense reasoning, revolutionary innovation and breakthroughs of the novel algorithms and new technologies  ...  This trough comes from the craze for the expert systems around 1980.  ... 
arXiv:2009.05678v1 fatcat:vn4pdl3k7jdwrmx4dygsyyfxvy

Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling [article]

Nishant Yadav, Sai Ravela, Auroop R. Ganguly
2020 arXiv   pre-print
A crucial question for data scientists in this context is the relevance of state-of-the-art data-driven approaches including those based on deep neural networks or kernel-based processes.  ...  the gains in predictive understanding with a suite of Deep Learning and strawman Linear Regression methods.  ...  Introduction With the advent of big data, machine learning and data science has ushered in a new era of predictive understanding of complex, highdimensional data in problems like image classification and  ... 
arXiv:2008.05590v1 fatcat:l2gnge47hbgf3fi2i65xxapm5y

Special Section Guest Editorial: Feature and Deep Learning in Remote Sensing Applications

John E. Ball, Derek T. Anderson, Chee Seng Chan
2018 Journal of Applied Remote Sensing  
processing; two papers utilizing spectral-spatial processing for hyperspectral image analysis; three papers on object tracking and recognition; one paper studying how deep networks need to be for remote  ...  For example, geospatial systems are plagued by factors like lack of (spatial, spectral, and temporal) labeled training data, high (spatial, spectral, and temporal) dimensionality, domain constraints (e.g  ...  and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and  ... 
doi:10.1117/1.jrs.11.042601 fatcat:pq3xg2sggfdtljjs3hrmp7tzdm

Quantum Artificial Intelligence for the Science of Climate Change [article]

Manmeet Singh, Chirag Dhara, Adarsh Kumar, Sukhpal Singh Gill, Steve Uhlig
2021 arXiv   pre-print
vast deluge of data at a scale of the planet Earth.  ...  Spatially and temporally localized forecasting is the need of the hour for effective adaptation measures towards minimizing the loss of life and property.  ...  resources for processing big data at the scale of planet Earth.  ... 
arXiv:2108.10855v2 fatcat:hamhfn6dhzcfhnk4i2gycvjoaa

Living in the Physics and Machine Learning Interplay for Earth Observation [article]

Gustau Camps-Valls, Daniel H. Svendsen, Jordi Cortés-Andrés, Álvaro Moreno-Martínez, Adrián Pérez-Suay, Jose Adsuara, Irene Martín, Maria Piles, Jordi Muñoz-Marí, Luca Martino
2020 arXiv   pre-print
data-driven models with physics-priors and dependence constraints, improve parameterizations, emulate physical models, and blend data-driven and process-based models.  ...  Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem.  ...  Learning ODEs with sparse regression As in many fields of science and engineering, Earth system models describe processes with a set of differential equations encoding our prior belief about the dynamics  ... 
arXiv:2010.09031v1 fatcat:74wsu52rync63j7as2rujrn2mq

Integrative mapping of global-scale processes and patterns on " imaginary Earth " continental geometries: A teaching tool in an Earth History course

David Sunderlin
2009 Journal of Geoscience education  
promoting group learning and increasing science communication skills.  ...  A synthesis capstone project has served to integrate patternbased learning of an introductory Earth History course into an active and process-based exercise in hypothesis production.  ...  We are, after all, quite reliant on our understanding of today's world in order to understand Earth systems throughout deep time.  ... 
doi:10.5408/1.3559527 fatcat:dgdekhq2a5f4zabbzcldu6oi2e
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