Reliable and timely crop-yield prediction and crop mapping are crucial for food security and
decision making in the food industry and in agro-environmental management. The global …

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 …

Segmentation is an essential step for remote sensing image processing. This study aims to
advance the application of the Segment Anything Model (SAM), an innovative image …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Distribution shifts occur when the test distribution differs from the training distribution, and
can considerably degrade performance of machine learning models deployed in the real …

It is commonplace to encounter heterogeneous data, of which some aspects of the data
distribution may vary but the underlying causal mechanisms remain constant. When data are …

Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …

The effective combination of the complementary information provided by huge amount of
unlabeled multisensor data (eg, synthetic aperture radar (SAR) and optical images) is a …

Currently, there is a growing population around the world, and this is particularly true in
developing countries, where food security is becoming a major problem. Therefore …

Machine learning methods for satellite data have a range of societally relevant applications,
but labels used to train models can be difficult or impossible to acquire. Self-supervision is a …