Geospatial foundation models (geo-FMs) are rapidly transforming the way we analyze and understand our planet. By leveraging vast amounts of satellite and geospatial data, these models provide a powerful foundation for tackling complex spatial challenges with unprecedented scale and efficiency. Similar to the role of large language models in natural language processing, geo-FMs serve as versatile starting points that can be fine-tuned for diverse applications, from environmental monitoring to climate adaptation. They significantly reduce the need for labeled data, improve the consistency of analysis over time, and open new opportunities for collaboration between science, government, and industry. However, since geodata is different from language, training geoFMs provides unique challenges. Spheer is a web-based Earth Observation platform powered by an in-house geospatial foundation model trained on time series of Sentinel-2 satellite data. On it, researchers, governments, land managers, and companies can develop custom monitoring solutions to analyze large areas and gain insights into biodiversity, agriculture, climate adaptation, infrastructure, and other spatial challenges. In several use cases, such as detecting specific grasses on Dutch heathlands indicative of nitrogen deposition, Spheer's geo-FM has demonstrated improved temporal stability and a significant reduction in required label data. Since September 2024, their small-data machine learning approach has been validated across dozens of use cases, both in the continental Netherlands and the Dutch Caribbean islands. This talk Jakko de Jong, director and cofounder of Spheer, will provide his views on the added value of foundation models, explain how geospatial foundation models work and share lessons learned while developing the Spheer foundation model. He will also showcase some existing use cases and demonstrate how foundation models open the road to a more iterative and playful way of interacting with satellite data.