Land cover change detection (LCCD) with remote sensing images is an important
application of Earth observation data because it provides insights into environmental health …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

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

Land cover change detection (LCCD) using remote sensing images (RSIs) plays an
important role in natural disaster evaluation, forest deformation monitoring, and wildfire …

We propose an unsupervised deep convolutional coupling network for change detection
based on two heterogeneous images acquired by optical sensors and radars on different …

The appetite for up-to-date information about earth's surface is ever increasing, as such
information provides a base for a large number of applications, including local, regional and …

Historically, change detection reviews have examined and categorized algorithms based on
their techniques for the remote sensing community. Here, we synthesize urban land change …

The importance of characterizing, quantifying, and monitoring land cover, land use, and their
changes has been widely recognized by global and environmental change studies. Since …

Land cover change monitoring is an important task from the perspective of regional resource
monitoring, disaster management, land development, and environmental planning. In this …

Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately
connected to many phases of the human and physical environment. Earth observation (EO) …