Bias in geospatial artificial intelligence (GeoAI) models has been documented, yet the evidence is scattered across narrowly focused studies. We synthesize this fragmented literature to provide a concise overview of bias in GeoAI and examine how the EU's Artificial Intelligence Act (EU AI Act) shapes audit obligations. We discuss recurring bias mechanisms, including representation, algorithmic and aggregation bias, and map them to specific provisions of the EU AI Act. By applying the Act's high-risk criteria, we demonstrate that widely deployed GeoAI applications qualify as high-risk systems. We then present examples of recent audits along with an outline of practical methods for detecting bias. As far as we know, this study represents the first integration of GeoAI bias evidence into the EU AI Act context, by identifying high-risk GeoAI systems and mapping bias mechanisms to the Act's Articles. Although the analysis is exploratory, it suggests that even well-curated European datasets should employ routine bias audits before 2027, when the AI Act's high-risk provisions take full effect.

Earth is enormous, and while humans have done a decent job of being able to map out the boundaries of countries and states, the roads in our cities and the location of geological sightseeing destinations, there remains a lot of the world that isn't precisely figured out. But a new project from Wenwen Li, associate professor in the School of Geographical Sciences and Urban Planning, aims to learn more about our world and its varying terrain by applying artificial intelligence. Artificial intelligence, or AI, has already made an indelible impact in daily life. From knowing our commutes or being able to suggest new shoes, what we divulge about ourselves and our habits has created a framework of information as it reveals hidden patterns in how we conduct our lives. The same can be true for our natural world as AI can help to reveal the patterns we haven't yet discovered.

Tony Frazier, president of Radiant Solutions, has said the company looks at the potential use of geospatial data to support artificial intelligence-based training efforts in the public sector, SpaceNews reported Saturday. He told the publication in an interview increasing the access to public data can help developers create open-source machine learning algorithms designed to recognize objects present in radar and optical imagery. "Our goal is to make data openly available to facilitate the creation of great algorithms that we can then apply at scale against commercial and government sources," added Frazier, a 2018 Wash100 recipient. Radiant, a business unit of Maxar Technologies, seeks to recruit professionals who possess data science, software development and geospatial analysis skills over the next year in a push to help address a demand for intelligence and mapping services in the defense market. SpaceNews estimates the Herndon, Va.-based contractor's work with the Defense Department, intelligence agencies and the U.S. Special Operations Command accounts for 90 percent of its annual $300 million revenue.