The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

Learning feature representations of geographical space is vital for any machine learning
model that integrates geolocated data, spanning application domains such as remote …

Research in self-supervised learning (SSL) with natural images has progressed rapidly in
recent years and is now increasingly being applied to and benchmarked with datasets …

Large Language Models (LLMs) have emerged as potent tools for advancing the United
Nations' Sustainable Development Goals (SDGs). However, the attitudinal disparities …

Forests play a crucial role in Earth's system processes and provide a suite of social and
economic ecosystem services, but are significantly impacted by human activities, leading to …

Foundation models have enormous potential in advancing Earth and climate sciences,
however, current approaches may not be optimal as they focus on a few basic features of a …

The volume of unlabelled Earth observation (EO) data is huge, but many important
applications lack labelled training data. However, EO data offers the unique opportunity to …

Forests are an essential part of Earth's ecosystems and natural systems, as well as providing
services on which humanity depends, yet they are rapidly changing as a result of land use …

In this white paper, we synthesize key points made during presentations and discussions
from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for …

Large, self-supervised vision models have led to substantial advancements for automatically
interpreting natural images. Recent works have begun tailoring these methods to remote …