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 …

Deep-learning (DL) algorithms, which learn the representative and discriminative features in
a hierarchical manner from the data, have recently become a hotspot in the machine …

Land cover (LC) map** is essential for monitoring the environment and understanding the
effects of human activities on it. Recent studies demonstrated successful applications of …

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level
representation in various computer vision tasks. However, in the field of remote sensing …

Advanced remote-sensing instruments produce massively large scenes from the surface of
the earth, with very high spatial resolution and dimensionality. Develo** methods for …

Semantic segmentation of high-resolution remote sensing images plays an important role in
the remote sensing community. However, many indistinguishable objects are prevalent …

Determining the poverty levels of various regions throughout the world is crucial in
identifying interventions for poverty reduction initiatives and directing resources fairly …

Convolutional neural networks (CNNs) have been successfully used in remote sensing
scene classification and identification due to their ability to capture deep spatial feature …

Contrastive learning techniques make it possible to pretrain a general model in a self-
supervised paradigm using a large number of unlabeled remote sensing images. The core …

Recently, the combination of remote sensing image processing and deep learning methods
is an increasingly popular trend. In this paper, we combine the existing instance …