ABSTRACT A large number of publications have incorporated deep learning in the process
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …

Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …

The rapid advancement of remote sensing technology has significantly enhanced the
temporal resolution of remote sensing data. Multitemporal remote sensing image …

The fully convolutional network (FCN) with an encoder-decoder architecture has been the
standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an …

As an important carrier of human productive activities, the extraction of buildings is not only
essential for urban dynamic monitoring but also necessary for suburban construction …

Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city
planning. A widely used method of obtaining accurate LULC maps is a classification of the …

Drought–land cover change (D-LCC) is considered to be an important stress factor that
affects vegetation greenness and productivity (VG&P) in global terrestrial ecosystems …

Convolutional neural networks (CNNs) are powerful in extracting local information but lack
the ability to model long-range dependencies. In contrast, the transformer relies on …

Deep-learning-based super-resolution (SR) methods have been extensively studied and
have achieved significant performance with deep convolutional neural networks. However …

The thriving development of earth observation technology makes more and more high-
resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the …