Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

The scaling of Transformers has driven breakthrough capabilities for language models. At
present, the largest large language models (LLMs) contain upwards of 100B parameters …

Large language models have emerged as a promising approach towards achieving general-
purpose AI agents. The thriving open-source LLM community has greatly accelerated the …

Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …

Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …

In recent years, remote-sensing scene classification is one of the research hotspots and has
played an important role in the field of intelligent interpretation of remote-sensing data …

Large-scale vision foundation models have made significant progress in visual tasks on
natural images, with vision transformers (ViTs) being the primary choice due to their good …

Deep learning-based algorithms have seen a massive popularity in different areas of remote
sensing image analysis over the past decade. Recently, transformer-based architectures …

Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …