Cloud cover assessment is crucial for meteorology, Earth observation, and environmental
monitoring, providing valuable data for weather forecasting, climate modeling, and remote …

Convolutional neural networks (CNN) have proven to be state of the art methods for many
image classification tasks and their use is rapidly increasing in remote sensing problems …

The systematic monitoring of the Earth using optical satellites is limited by the presence of
clouds. Accurately detecting these clouds is necessary to exploit satellite image archives in …

The Landsat record represents an amazing resource for discovering land-cover changes
and monitoring the Earth's surface. However, making the most use of the available data …

Cloud contamination represents a large obstacle for mapping the earth's surface using
remotely sensed data. Therefore, cloudy pixels should be identified and eliminated before …

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical
spaceborne remote sensing sensors. Multiple methods have been developed to address the …

Cloud masking is of central importance to the Earth Observation community. This paper
deals with the problem of detecting clouds in visible and multispectral imagery from high …

Nowadays, many automated satellite-based monitoring systems (eg for forestry or
agriculture) widely use the images from Sentinel-2 and PlanetScope satellites, which …

Cloud masking is a crucial task that is well-motivated for meteorology and its applications in
environmental and atmospheric sciences. Its goal is, given satellite images, to accurately …

Cloud masking is a very important application in remote sensing and an essential pre-
processing step for any information derivation applications. It helps in estimation of usable …