ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 317–324, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-317-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 317–324, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-317-2021

  17 Jun 2021

17 Jun 2021

JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY

T. Stomberg1, I. Weber2, M. Schmitt3, and R. Roscher1 T. Stomberg et al.
  • 1Institute of Geodesy and Geoinformation, University of Bonn, Germany
  • 2AMLS, University of Applied Sciences Koblenz, Germany
  • 3Department of Geoinformatics, Munich University of Applied Sciences, Germany

Keywords: Scene classification, Explainability, Interpretability, Deep neural networks

Abstract. Explainable machine learning has recently gained attention due to its contribution to understanding how a model works and why certain decisions are made. A so far less targeted goal, especially in remote sensing, is the derivation of new knowledge and scientific insights from observational data. In our paper, we propose an explainable machine learning approach to address the challenge that certain land cover classes such as wilderness are not well-defined in satellite imagery and can only be used with vague labels for mapping. Our approach consists of a combined U-Net and ResNet-18 that can perform scene classification while providing at the same time interpretable information with which we can derive new insights about classes. We show that our methodology allows us to deepen our understanding of what makes nature wild by automatically identifying simple concepts such as wasteland that semantically describes wilderness. It further quantifies a class’s sensitivity with respect to a concept and uses it as an indicator for how well a concept describes the class.