Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …

BACKGROUND Accurate and comprehensive measurements of a range of sustainable
development outcomes are fundamental inputs into both research and policy. For instance …

While reliable data-driven decision-making hinges on high-quality labeled data, the
acquisition of quality labels often involves laborious human annotations or slow and …

Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …

The origin of the COVID-19 pandemic has given overture to redirection, as well as
innovation to many digital technologies. Even after the progression of vaccination efforts …

Metasurfaces, interacted with artificial intelligence, have now been motivating many
contemporary research studies to revisit established fields, eg, direction of arrival (DOA) …

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 …

Earth observation (EO) is a prime instrument for monitoring land and ocean processes,
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …

Remotely sensed geospatial data are critical for applications including precision agriculture,
urban planning, disaster monitoring and response, and climate change research, among …

This paper is a methodological guide to using machine learning in the spatial context. It
provides an overview of the existing spatial toolbox proposed in the literature: unsupervised …