The field of Earth observation and remote sensing is moving towards the development of more efficient and accurate systems for data analysis and processing. Researchers are focusing on creating synergistic systems that combine the strengths of satellite and ground-based systems to enable near real-time Earth observation applications. Another significant direction is the development of vision-language models that can handle pixel-level tasks and perform fine-grained remote sensing image parsing. These models are being designed to be more efficient and adaptable to different tasks and modalities. Noteworthy papers in this area include:

• A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation, which proposes an efficient satellite-ground synergistic LVLM inference system.
• GeoMag, which introduces a novel framework for remote sensing image parsing that dynamically focuses attention scope based on prompt semantics.
• MAPEX, which proposes a remote sensing foundation model based on mixture-of-modality experts that can be efficiently pruned for specific tasks.