Abstract

The use of foundation models produced from geospatial data, such as satellite images, is becoming increasingly common in ‘remote-sensing’ methods deployed by journalists alongside traditional reporting methods of field visits, observation and interviews. This presentation discusses the lessons learned from a project undertaken by CDH in collaboration with the Pulitzer Center and Watershed Investigations to deploy EarthIndex, a tool which combines code to produce cloud-free composite Sentinel-2 images and run foundation model inference on 32×32-pixel patches worldwide with an interface allowing users to label images and generate fast approximate nearest neighbour searches from a vector database. We were able to test model predictions against investigation and observation at ground level and demonstrate the importance of triangulating information derived from the use of AI tools with other sources and perspectives. Our team members’ field work also underlined the ethical challenges of relying on remote sensing data and the potential for the use of such methods to harm vulnerable communities.