With a growing reliance on machine learning to help find patterns in the ever-growing volumes of environmental data, we utilize cutting-edge analytics such as statistics, deep learning, and artificial intelligence.

Open source software can create data tools that streamline data access, pre-processing, and curation into standard formats that enable custom, value-added analytics.

We use novel analytics for curating cross-scale datasets that integrate these observations to help us better resolve and understand underlying ecological processes.

Modern science sometimes requires computers with much more power than is available from your average personal computer. At Earth Lab we develop and deploy our analyses in cloud environments to more easily scale our science.

Drones equipped with high resolution multispectral and RGB cameras can observe the natural environment at a much finer spatial and temporal scale than possible with most satellite imagery.

At Earth Lab, we often use data from diverse sources to facilitate inquiry, from the more conventional remote sensing datasets such as multispectral satellite imagery and radar backscatter, airborne lidar data, and high-resolution UAV imagery, to the less traditional datasets such as social media feeds, housing layers, and event databases.

From centimeter-scale imagery collected from UAVs, to airborne hyperspectral imagery at the meter-scale, to the 10’s of meter scale from satellite multispectral imaging systems, the diversity of data representing the Earth’s surface at different scales enables us to ask questions from the hyperlocal to continental and global scale. We combine these data to better understand processes and change occurring on the Earth.