Artificial intelligence and machine learning are ubiquitous in the domain of Earth
Observation (EO) and Remote Sensing. Congruent to their success in the domain of …

Explainable artificial intelligence (XAI) methods shed light on the predictions of machine
learning algorithms. Several different approaches exist and have already been applied in …

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience
because of their ability to capture nonlinear system behavior and extract predictive …

In recent years, artificial intelligence and specifically artificial neural networks (NNs) have
shown great success in solving complex, nonlinear problems in earth sciences. Despite their …

Methods of explainable artificial intelligence (XAI) are used in geoscientific applications to
gain insights into the decision-making strategy of neural networks (NNs), highlighting which …

Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over
the last few years. This is due to the widespread application of machine learning, particularly …

Earth observation (EO) is a prime instrument for monitoring land and ocean processes,
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …

We present a method of explainable artificial intelligence (XAI),“What I Know (WIK)”, to
provide additional information to verify the reliability of a deep learning model by showing an …

Issues regarding explainable AI involve four components: users, laws and regulations,
explanations and algorithms. Together these components provide a context in which …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …