Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …

This study is conducted in accordance with a systematic literature review (SLR) protocol.
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …

Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …

Labeled samples are important in achieving land cover change detection (LCCD) tasks via
deep learning techniques with remote sensing images. However, labeling samples for …

Deep learning has substantially pushed forward remote sensing image change detection
through extracting discriminative hierarchical features. However, as the increasingly high …

In remote sensing images, change detection (CD) is required in many applications, such as:
resource management, urban expansion research, land management, and disaster …

Change detection is of great significance to Earth observations. Recently, with the
emergence of deep learning (DL), the power and feasibility of deep convolutional neural …

Buildings are some of the basic spatial elements of a city. Changes in the spatial
distributions of buildings are of great significance for urban planning and monitoring illegal …

Building change detection (BCD) plays a crucial role in urban planning and development
and has received extensive attention. However, existing deep learning-based change …