The field of geospatial and health informatics is witnessing significant developments, driven by the increasing availability of large datasets and advancements in machine learning and foundation models. Researchers are exploring innovative applications of these models to address real-world challenges, such as flood susceptibility mapping, crop type mapping, and health facility programmatic output prediction. Notably, the integration of multimodal data and the use of transfer learning are enabling more accurate and efficient predictions. Furthermore, the development of new architectures and techniques, such as vision language foundation models and diffusion priors, is expanding the capabilities of geospatial analysis. Overall, these advancements have the potential to transform various fields, including disaster prevention, precision agriculture, and healthcare. Noteworthy papers include: The SARCLIP paper, which introduces a vision language foundation model for semantic understanding and target recognition in SAR imagery, demonstrating superior performance in feature extraction and interpretation. The ZeroFlood paper, which presents a geospatial foundation model framework for data-efficient flood susceptibility mapping, achieving state-of-the-art results with limited training data.