Machine Learning Approach to Automated Construction of Knowledge Bases For Expert Systems for Remote Sensing Image Analysis

1996 ◽  
Vol 11 (4) ◽  
pp. VIII-IX ◽  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Image Analysis ◽  
Expert Systems ◽  
Remote Sensing Image ◽  
Knowledge Bases ◽  
Learning Approach ◽  
Construction Of Knowledge ◽  
Machine Learning Approach

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

2021 ◽  
Vol V-3-2021 ◽  
pp. 317-324
Author(s):  
T. Stomberg ◽  
I. Weber ◽  
M. Schmitt ◽  
R. Roscher
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Observational Data ◽  
Satellite Imagery ◽  
Learning Approach ◽  
Scene Classification ◽  
Machine Learning Approach ◽  
New Knowledge

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.

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