With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Remote sensing (RS) plays an important role gathering data in many critical domains (eg,
global climate change, risk assessment and vulnerability reduction of natural hazards …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

Longer time high-resolution, high-frequency, consistent, and more detailed land cover data
are urgently needed in order to achieve sustainable development goals on food security …

Recently, the first foundation model developed specifically for image segmentation tasks
was developed, termed the" Segment Anything Model"(SAM). SAM can segment objects in …

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 …

The booming development of remote sensing images in many visual tasks has led to an
increasing demand for obtaining images with more precise details. However, it is impractical …

About half of all optical observations collected via spaceborne satellites are affected by haze
or clouds. Consequently, cloud coverage affects the remote-sensing practitioner's …

The majority of optical observations acquired via spaceborne Earth imagery are affected by
clouds. While there is numerous prior work on reconstructing cloud-covered information …

The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture
Radar (SAR) images that can penetrate cloud cover. However, the large domain gap …