With the fast development of remote sensing platforms and sensors technology, change
detection with heterogeneous remote sensing images (Hete-CD) has become an attractive …

Upon the introduction of machine learning (ML) and its variants, in the form that we know
today, to the landslide community, many studies have been carried out to explore the …

As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

This study presents a new end-to-end change detection network, called difference-
enhancement dense-attention convolutional neural network (DDCNN), that is designed for …

Landslides are geomorphological processes that shape the landscapes of all continents,
dismantling mountains and contributing sediments to the river networks. Caused by …

Benefiting from the developments in deep learning technology, deep-learning-based
algorithms employing automatic feature extraction have achieved remarkable performance …

Mapping landslides using automated methods is a challenging task, which is still largely
done using human efforts. Today, the availability of high-resolution EO data products is …

Deep learning for change detection is one of the current hot topics in the field of remote
sensing. However, most end-to-end networks are proposed for supervised change …

Landslide mapping via pixel-wise classification of remote sensing imagery is essential for
hazard prevention and risk assessment. Deep-learning-based change detection greatly aids …

Land cover change detection (LCCD) with very high-resolution remote-sensing images
(VHR_RSIs) is important in observing surface change on Earth. However, pseudo-changes …