In plant sciences, we use aerial and hyperspectral imaging to monitor forest health and identify invasive species by analyzing the spectral properties of trees.

We partner with the largest satellite companies to source high-quality imagery and collect lidar data, which is crucial for improving our algorithms and expanding our model globally.

We're using drones to analyze the built environment, focusing on processing lines rather than points, which is traditionally more challenging in computer vision.

In CT imaging, what is measured are projections of a 3D object, and the goal is to reconstruct the object from these projections.

Our platform allows users to see detailed information about forest projects, including satellite images, photos, and descriptions of co-benefits like biodiversity and water conservation.

By integrating a 3D representation into models, we can better align the model's capabilities with the tasks we want it to perform, enhancing user affordances. Spatial intelligence is crucial for creating technology that understands and interacts with the 3D world, enabling a wide range of applications.

Plants can sense light and use it to determine the shape and nutritional content of other plants, influencing their growth direction.

Pachama uses remote sensing and artificial intelligence for verification and monitoring of carbon storage in forests, increasing project credibility and efficiency.

The Maps team utilized a similar model to assess solar energy potential on rooftops by identifying rooftops in images, demonstrating the versatility of our models.