The field of geospatial analysis and multimodal learning is advancing rapidly, with a focus on developing innovative methods to capture complex relationships between spatial data and other modalities. Researchers are exploring new distance metrics, such as geodesic distance, to improve the accuracy of multimodal learning models. Additionally, there is a growing interest in incorporating geographic information into large language models to enhance their ability to understand spatial contexts. Noteworthy papers include GeoMM, which introduces a geodesic distance metric for multimodal learning, and GA-LLM, which proposes a geography-aware large language model for next POI recommendation. These advancements have the potential to significantly improve the performance of geospatial analysis and multimodal learning models, enabling more accurate predictions and better decision-making in a wide range of applications.