The field of remote sensing is witnessing a significant shift towards the development of more versatile and generalizable models. Researchers are moving away from task-isolated approaches and towards unified frameworks that can handle multiple tasks and domains. This is evident in the increasing use of multi-task learning and generative models that can synthesize remote sensing imagery for various high-level vision tasks. Additionally, there is a growing emphasis on bridging the gap between different data sources and modalities, such as laboratory and satellite spectra, to enable more accurate and large-scale analysis. The use of knowledge distillation and domain adaptation techniques is also becoming more prevalent in this area. Noteworthy papers in this regard include TerraGen, which introduces a unified layout-to-image generation framework for remote sensing data augmentation, and SITS-DECO, which applies a unified-sequence framing to EO data using a simple GPT-style decoder-only architecture. Other notable papers include DeepSalt, which bridges laboratory and satellite spectra for large-scale soil salinity estimation, and CYPRESS, which leverages a pre-trained geospatial foundation model for high-resolution crop yield prediction.