Explainable AI (XAI) is becoming increasingly vital in remote sensing due to the reliance on complex models for data analysis. As stakeholders often depend on model predictions for significant decisions, such as disaster response or public health measures, understanding how these models arrive at their conclusions is critical. XAI provides transparent explanations of model behavior, allowing users to know which features influence decisions, thus improving trust and model adoption in applications like climate monitoring and precision agriculture.
XAI techniques in remote sensing aim to illuminate the workings of black-box models, enhancing stakeholder trust in AI recommendations for crucial decisions.
Explanations from XAI can reveal input features that influence model outputs, which is vital for applications directly impacting public health and safety.
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