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Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection
Chia Yu Hsu,
Wenwen Li
Geographical Sciences and Urban Planning, School of (SGSUP)
Research output
:
Contribution to journal
›
Article
›
peer-review
6
Scopus citations
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Dive into the research topics of 'Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection'. Together they form a unique fingerprint.
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Earth & Environmental Sciences
natural feature
100%
artificial intelligence
84%
learning
60%
detection
43%
prediction
12%
image processing
11%
method
10%
pixel
9%
experiment
4%
analysis
3%
Engineering & Materials Science
Artificial intelligence
65%
Deep learning
33%
Gradient methods
14%
Object recognition
12%
Backpropagation
11%
Chemical activation
11%
Image processing
9%
Pixels
9%
Experiments
4%
Social Sciences
artificial intelligence
74%
learning process
56%
learning
12%
activation
12%
experiment
7%