Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …

Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …

The abrupt rise in extreme weather events (floods, heat waves, droughts, etc.) due to
changing climate in the last decades has increased the level of threats to various sectors …

We present a significantly improved data‐driven global weather forecasting framework using
a deep convolutional neural network (CNN) to forecast several basic atmospheric variables …

Modeling weather and climate is an essential endeavor to understand the near-and long-
term impacts of climate change, as well as to inform technology and policymaking for …

We develop elementary weather prediction models using deep convolutional neural
networks (CNNs) trained on past weather data to forecast one or two fundamental …

Accurate and high-resolution data reflecting different climate scenarios are vital for policy
makers when deciding on the development of future energy resources, electrical …

Deep learning techniques (in particular convolutional neural networks, CNNs) have recently
emerged as a promising approach for statistical downscaling due to their ability to learn …

Artificial Intelligence (AI) is an explosively growing field of computer technology, which is
expected to transform many aspects of our society in a profound way. AI techniques are …