Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research

Martin Werner; Gabriel Dax; Moritz Laass

doi:10.18420/inf2020_95

Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research

Martin Werner, Gabriel Dax, Moritz Laass

Assistant Professorship of Big Geospatial Data Management

Technical University of Munich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In the last decades, environmental research has started to adopt a data-driven perspective enabled by huge sensor networks, satellite-based Earth observation, and almost ubiquitous Internet access. Some of these data-driven approaches are expected to make visions of a sustainable future come true. For example, by enabling societies to live in sustainable smart cities, or to feed the world with precision agriculture. Or by fighting environmental pollution or global deforestation with increased observational power. However, there is a serious gap between some of the current expectations put into data-driven techniques and the maturity of the field of spatial machine learning and artificial intelligence or computer science in general. We give a few examples of open research issues that computer science has to solve in order to make data-driven approaches to environmental sciences successful.

Original language	English
Title of host publication	50. Jahrestagung der Gesellschaft fur Informatik
Subtitle of host publication	Back to the Futures, INFORMATIK 2020
Editors	Ralf H. Reussner, Anne Koziolek, Robert Heinrich
Publisher	Gesellschaft fur Informatik (GI)
Pages	1009-1017
Number of pages	9
ISBN (Electronic)	9783885797012
DOIs	https://doi.org/10.18420/inf2020_95
State	Published - 2020
Event	50. Jahrestagung der Gesellschaft fur Informatik, INFORMATIK 2020 - 50th Annual Conference of the German Informatics Society, INFORMATIK 2020 - Karlsruhe, Germany Duration: 28 Sep 2020 → 2 Oct 2020

Publication series

Name	Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
Volume	P-307
ISSN (Print)	1617-5468

Conference

Conference	50. Jahrestagung der Gesellschaft fur Informatik, INFORMATIK 2020 - 50th Annual Conference of the German Informatics Society, INFORMATIK 2020
Country/Territory	Germany
City	Karlsruhe
Period	28/09/20 → 2/10/20

Keywords

Environmental Sciences; Computer Sciences

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.18420/inf2020_95

Cite this

Werner, M., Dax, G., & Laass, M. (2020). Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research. In R. H. Reussner, A. Koziolek, & R. Heinrich (Eds.), 50. Jahrestagung der Gesellschaft fur Informatik: Back to the Futures, INFORMATIK 2020 (pp. 1009-1017). (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); Vol. P-307). Gesellschaft fur Informatik (GI). https://doi.org/10.18420/inf2020_95

Werner, Martin ; Dax, Gabriel ; Laass, Moritz. / Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research. 50. Jahrestagung der Gesellschaft fur Informatik: Back to the Futures, INFORMATIK 2020. editor / Ralf H. Reussner ; Anne Koziolek ; Robert Heinrich. Gesellschaft fur Informatik (GI), 2020. pp. 1009-1017 (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)).

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title = "Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research",

abstract = "In the last decades, environmental research has started to adopt a data-driven perspective enabled by huge sensor networks, satellite-based Earth observation, and almost ubiquitous Internet access. Some of these data-driven approaches are expected to make visions of a sustainable future come true. For example, by enabling societies to live in sustainable smart cities, or to feed the world with precision agriculture. Or by fighting environmental pollution or global deforestation with increased observational power. However, there is a serious gap between some of the current expectations put into data-driven techniques and the maturity of the field of spatial machine learning and artificial intelligence or computer science in general. We give a few examples of open research issues that computer science has to solve in order to make data-driven approaches to environmental sciences successful.",

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series = "Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)",

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Werner, M, Dax, G & Laass, M 2020, Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research. in RH Reussner, A Koziolek & R Heinrich (eds), 50. Jahrestagung der Gesellschaft fur Informatik: Back to the Futures, INFORMATIK 2020. Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), vol. P-307, Gesellschaft fur Informatik (GI), pp. 1009-1017, 50. Jahrestagung der Gesellschaft fur Informatik, INFORMATIK 2020 - 50th Annual Conference of the German Informatics Society, INFORMATIK 2020, Karlsruhe, Germany, 28/09/20. https://doi.org/10.18420/inf2020_95

Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research. / Werner, Martin; Dax, Gabriel; Laass, Moritz.
50. Jahrestagung der Gesellschaft fur Informatik: Back to the Futures, INFORMATIK 2020. ed. / Ralf H. Reussner; Anne Koziolek; Robert Heinrich. Gesellschaft fur Informatik (GI), 2020. p. 1009-1017 (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); Vol. P-307).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Dax, Gabriel

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PY - 2020

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N2 - In the last decades, environmental research has started to adopt a data-driven perspective enabled by huge sensor networks, satellite-based Earth observation, and almost ubiquitous Internet access. Some of these data-driven approaches are expected to make visions of a sustainable future come true. For example, by enabling societies to live in sustainable smart cities, or to feed the world with precision agriculture. Or by fighting environmental pollution or global deforestation with increased observational power. However, there is a serious gap between some of the current expectations put into data-driven techniques and the maturity of the field of spatial machine learning and artificial intelligence or computer science in general. We give a few examples of open research issues that computer science has to solve in order to make data-driven approaches to environmental sciences successful.

AB - In the last decades, environmental research has started to adopt a data-driven perspective enabled by huge sensor networks, satellite-based Earth observation, and almost ubiquitous Internet access. Some of these data-driven approaches are expected to make visions of a sustainable future come true. For example, by enabling societies to live in sustainable smart cities, or to feed the world with precision agriculture. Or by fighting environmental pollution or global deforestation with increased observational power. However, there is a serious gap between some of the current expectations put into data-driven techniques and the maturity of the field of spatial machine learning and artificial intelligence or computer science in general. We give a few examples of open research issues that computer science has to solve in order to make data-driven approaches to environmental sciences successful.

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Werner M, Dax G, Laass M. Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research. In Reussner RH, Koziolek A, Heinrich R, editors, 50. Jahrestagung der Gesellschaft fur Informatik: Back to the Futures, INFORMATIK 2020. Gesellschaft fur Informatik (GI). 2020. p. 1009-1017. (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)). doi: 10.18420/inf2020_95

Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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