Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from
input data through a model. This problem has attracted increasing attention in the area of …

In the past decade, deep learning has been widely applied across various fields. However,
its applicability in open-world scenarios is often limited due to the lack of quantifying …

Earth Observation imagery can capture rare and unusual events such as disasters and
major landscape changes whose visual appearance contrasts with the usual observations …

Automated forest map** is important to understand our forests that play a key role in
ecological system. However, efforts towards forest map** is impeded by difficulty to collect …

Reliability quantification of deep reinforcement learning (DRL)-based control is a significant
challenge for the practical application of artificial intelligence (AI) in safety-critical systems …

Earth observation relies on a diverse array of sensors with significant variations in design
and capabilities, spanning from high-resolution optical images to thermal data and radar …

Deep learning models for image classification are typically trained under the" closed-world"
assumption with a predefined set of image classes. However, when the models are …