Chapter 4: Data Analysis and Exploration with Computational Approaches

Wichert, Viktoria; Bouwer, Laurens M.; Abraham, Nicola; Brix, Holger; Callies, Ulrich; González Ávalos, Everardo; Marien, Lennart Christopher; Matthias, Volker; Michaelis, Patrick; Rabe, Daniela; Rechid, Diana; Ruhnke, Roland; Scharun, Christian; Valizadeh, Mahyar; Vlasenko, Andrey; Graf zu Castell-Rüdenhausen, Wolfgang

doi:10.1007/978-3-030-99546-1_4

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Chapter 4: Data Analysis and Exploration with Computational Approaches

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Wichert, Viktoria
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Bouwer, Laurens M.
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Abraham, Nicola
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Brix, Holger
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Callies, Ulrich
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González Ávalos, Everardo
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Marien, Lennart Christopher
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Matthias, Volker
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Michaelis, Patrick
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Rabe, Daniela
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Rechid, Diana
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Ruhnke, Roland
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Scharun, Christian
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Valizadeh, Mahyar
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Vlasenko, Andrey
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Graf zu Castell-Rüdenhausen, Wolfgang
5.0 Geoinformation, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Wichert, V., Bouwer, L. M., Abraham, N., Brix, H., Callies, U., González Ávalos, E., Marien, L. C., Matthias, V., Michaelis, P., Rabe, D., Rechid, D., Ruhnke, R., Scharun, C., Valizadeh, M., Vlasenko, A., Graf zu Castell-Rüdenhausen, W. (2022): Chapter 4: Data Analysis and Exploration with Computational Approaches. - In: Bouwer, L. M., Dransch, D., Ruhnke, R., Rechid, D., Frickenhaus, S., Greinert, J. (Eds.), Integrating Data Science and Earth Science Challenges and Solutions, (SpringerBriefs in Earth System Sciences), Cham : Springer International Publishing, 29-54.
https://doi.org/10.1007/978-3-030-99546-1_4

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5013594

Abstract

Artificial intelligence and machine learning (ML) methods are increasinglyappliedinEarthsystemresearch,forimprovingdataanalysis,andmodelperformance,andeventuallysystemunderstanding.IntheDigitalEarthproject,severalML approaches have been tested and applied, and are discussed in this chapter. These include data analysis using supervised learning and classification for detection of river levees and underwater ammunition; process estimation of methane emissions andforenvironmentalhealth;point-to-spaceextrapolationofvaryingobservedquantities; anomaly and event detection in spatial and temporal geoscientific datasets. We present the approaches and results, and finally, we provide some conclusions on the broad applications of these computational data exploration methods and approaches.