In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

In this study, we propose a novel unsupervised Change Detection (CD) model to detect
flood extent using Synthetic Aperture Radar (SAR) time series data. The proposed model is …

Applications such as disaster management enormously benefit from rapid availability of
satellite observations. Traditionally, data analysis is performed on the ground after being …

The development and adoption of artificial intelligence (AI) technologies in space
applications is growing quickly as the consensus increases on the potential benefits …

While novel artificial intelligence and machine learning techniques are evolving and
disrupting established terrestrial technologies at an unprecedented speed, their adaptation …

Human civilization has an increasingly powerful influence on the earth system. Affected by
climate change and land-use change, natural disasters such as flooding have been …

Satellite data transmission is a crucial bottleneck for Earth observation applications. To
overcome this problem we propose a novel solution that trains a neural network on board …

Artificial intelligence onboard satellites has the potential to reduce data transmission
requirements, enable real-time decision-making and collaboration within constellations. This …

Nowadays, most of the datasets leveraging space-borne Earth Observation (EO) data are
based on high-end levels products, which are ortho-rectified, coregistered, calibrated, and …

Sudden-onset Earth's surface anomalies, such as natural disasters and man-made
incidents, pose severe threats to human life and property security, emphasizing the crucial …