The presence of clouds prevents optical satellite imaging systems from obtaining useful
Earth observation information and negatively affects the processing and application of …

Spectral Indices derived from multispectral remote sensing products are extensively used to
monitor Earth system dynamics (eg vegetation dynamics, water bodies, fire regimes). The …

Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a
category to each pixel in remote sensing images, plays a vital role in a broad range of …

Identifying high-quality (ie, relatively clear) measurements of surface conditions is a near-
universal first step in working with optical satellite imagery. Many cloud masking algorithms …

Cloud detection is a pivotal satellite image pre-processing step that can be performed on
board a satellite to tag useful images. It can reduce the amount of data to downlink by …

Accurately characterizing clouds and their shadows is a long-standing problem in the Earth
Observation community. Recent works showcase the necessity to improve cloud detection …

The relationship between yield and quality of grassland fodder is an important factor for
livestock farmers to consider when deciding the timing of grassland cuts. Even for …

Recently, deep learning has been widely used for cloud detection in satellite images;
however, due to radiometric and spatial resolution differences in images from different …

We present a novel approach for modeling vegetation response to weather in Europe as
measured by the Sentinel 2 satellite. Existing satellite imagery forecasting approaches focus …

Mapping of highly dynamic changes in land use and land cover (LULC) can be hindered by
various cloudy conditions with optical satellite images. These conditions result in …