Chapter 12 - Explainable AI for understanding ML-derived vegetation products
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Abstract
Current machine learning applications and algorithms have developed promise to produce autonomous systems that automatically perceive, learn, predict, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions, algorithmic paths, and actions to human users. The purpose of this chapter is to apply explainable artificial intelligence (XAI) to black-box models using an example of the U.S. Geological Survey's LANDFIRE Existing Vegetation Type (EVT). This chapter also demonstrates the tools developed to assist scientists/analysts in understanding and trusting prediction outcomes of vegetation type that streamline development of the LANDFIRE EVT product.
| Publication type | Book chapter |
|---|---|
| Publication Subtype | Book Chapter |
| Title | Chapter 12 - Explainable AI for understanding ML-derived vegetation products |
| DOI | 10.1016/B978-0-323-91737-7.00008-6 |
| Publication Date | April 23, 2023 |
| Year Published | 2023 |
| Language | English |
| Publisher | Elsevier Inc |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 19 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Monograph |
| Larger Work Title | Artificial intelligence in earth science: Best practices and fundamental challenges |
| First page | 317 |
| Last page | 335 |