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  4. Explain it to me - facing remote sensing challenges in the bio- and geosciences with explainable machine learning
 
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2020
Conference Paper
Title

Explain it to me - facing remote sensing challenges in the bio- and geosciences with explainable machine learning

Abstract
For some time now, machine learning methods have been indispensable in many application areas. Especially with the recent development of efficient neural networks, these methods are increasingly used in the sciences to obtain scientific outcomes from observational or simulated data. Besides a high accuracy, a desired goal is to learn explainable models. In order to reach this goal and obtain explanation, knowledge from the respective domain is necessary, which can be integrated into the model or applied post-hoc. We discuss explainable machine learning approaches which are used to tackle common challenges in the bio- and geosciences, such as limited amount of labeled data or the provision of reliable and scientific consistent results. We show that recent advances in machine learning to enhance transparency, interpretability, and explainability are helpful in overcoming these challenges.
Author(s)
Roscher, R.
Institute of Geodesy and Geoinformation, University of Bonn, Germany
Bohn, B.
Institute for Numerical Simulation, University of Bonn, Germany
Duarte, M.F.
Department of Electrical and Computer Engineering, University of Massachusetts Amherst, USA
Garcke, J.
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Mainwork
XXIV ISPRS Congress 2020. Commission III  
Conference
International Society for Photogrammetry and Remote Sensing (ISPRS Congress) 2020  
Open Access
DOI
10.5194/isprs-annals-V-3-2020-817-2020
Language
English
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Keyword(s)
  • machine learning

  • explainability

  • interpretability

  • geoscience

  • biosciences

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