Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …

Large language models (LLMs) have significantly advanced the field of natural language
processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of …

Humans live in a 3D world and commonly use natural language to interact with a 3D scene.
Modeling a 3D language field to support open-ended language queries in 3D has gained …

Segmentation is an essential step for remote sensing image processing. This study aims to
advance the application of the Segment Anything Model (SAM), an innovative image …

This paper presents a comprehensive examination of how multimodal artificial intelligence
(AI) approaches are paving the way towards the realization of Artificial General Intelligence …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Geo-tagged images are publicly available in large quantities, whereas labels such as object
classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has …

General-purpose foundation models have led to recent breakthroughs in artificial
intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image …

Large language models (LLMs) have achieved great success in general domains of natural
language processing. In this paper, we bring LLMs to the realm of geoscience with the …

Artificial general intelligence (AGI) has gained global recognition as a future technology due
to the emergence of breakthrough large language models and chatbots such as GPT-4 and …