Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Earth observation (EO) satellite missions have been providing detailed images about the
state of Earth and its land cover for over 50 years. Long-term missions, such as those of …

Multisensor data analysis allows exploiting heterogeneous data regularly acquired by the
many available remote sensing (RS) systems. Machine-and deep-learning methods use the …

Floods occur throughout the world and are becoming increasingly frequent and dangerous.
This is due to different factors, among which climate change and land use stand out. In …

Floods are the most common phenomenon and cause the most significant economic and
social damage to the population. They are becoming more frequent and dangerous …

Satellite image time series (SITS) classification is a challenging application concurrently
driven by long-term, large-scale, and high spatial-resolution observations acquired by …

Timely and accurate Land Cover (LC) information is required for various applications, such
as climate change analysis and sustainable development. Although machine learning …

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at high spatial
resolution and high revisit time, respectively, radar and optical images that support a wide …

In the computerized world the uniqueness of information security is fundamental. Biometric
frameworks utilizing individual physiological or conduct ascribes which are turning out to be …

In this article, we propose a method exploiting irregular and unaligned Sentinel-2 satellite
image time series (SITS) for large-scale land cover pixel-based classification. We perform …