Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Modality is a source or form of information. Through various modal information, humans can
perceive the world from multiple perspectives. Simultaneously, the observation of remote …

We consider the problem of inferring high-dimensional data $ x $ in a model that consists of
a prior $ p (x) $ and an auxiliary differentiable constraint $ c (x, y) $ on $ x $ given some …

Remote sensing images are useful for a wide variety of planet monitoring applications, from
tracking deforestation to tackling illegal fishing. The Earth is extremely diverse---the amount …

Deep learning-based coastline detection algorithms have begun to outshine traditional
statistical methods in recent years. However, they are usually trained only as single-purpose …

The U-net is nowadays among the most popular deep learning algorithms for land use/land
cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar …

Recent progress in self-supervision has shown that pre-training large neural networks on
vast amounts of unsupervised data can lead to substantial increases in generalization to …

As an important application in remote sensing, landcover classification remains one of the
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …

Large-scale high-resolution land-cover mapping is a way to comprehend the Earth's surface
and resolve the ecological and resource challenges facing humanity. High-resolution (≤ 1 …

Monitoring of land cover and land use is crucial in natural resources management.
Automatic visual mapping can carry enormous economic value for agriculture, forestry, or …