Abstract Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense
potential towards a generic model for Earth Observation. Nevertheless these works primarily …

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 …

Federated learning (FL) enables the collaboration of multiple deep learning (DL) models to
learn from decentralized data archives (ie, clients) without accessing data on the clients …

Artificial intelligence models encounter significant challenges due to their black-box nature,
particularly in safety-critical domains such as healthcare, finance, and autonomous vehicles …

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 …

Massive amounts of unlabelled data are captured by Earth Observation (EO) satellites, with
the Sentinel-2 constellation generating 1.6 TB of data daily. This makes Remote Sensing a …

Monitoring forests, in particular their response to climate and land use change, requires
studying long time scales. While efficient deep learning methods have been developed to …

As large-scale foundation models become publicly available for different domains efficiently
adapting them to individual downstream applications and additional data modalities has …

In the face of climate change and population growth, Local Climate Zone (LCZ) maps have
emerged as crucial tools for urban planners and policymakers to address Urban Heat Island …

This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote
sensing dataset constructed to support deep learning (DL) studies for remote sensing image …