MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data ...

High-precision transportation intelligence, powered by Nearmap high-recency aerial imagery and Ecopia's AI feature extraction, delivers decision-ready geospatial context at scale SALT LAKE CITY, Jan.

During the COVID-19 pandemic, Essex County Health Officer Maya Harlow wished she had a crystal ball that could predict where ...

Dr Michele Orini shares how machine learning can help identify critical VT ablation targets for a safer, data-driven ...

The IMF study shows that satellite data such as nighttime lights, air pollution, and vegetation health, when combined with machine-learning models, can reliably estimate Cambodia’s GDP growth in near ...

An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...

Researchers at Los Alamos National Laboratory have developed a new approach that addresses the limitations of generative AI ...

Abstract: Accurately modeling and predicting urban path loss is a challenging task due to the fact that conventional regression models yield continuous estimates that can hide important performance ...

Urban gender-based violence (GBV) is not randomly distributed across city space, and new research shows that advanced geospatial machine learning can identify where risks concentrate with striking ...

Abstract: The way we handle spatial data has changed significantly as a result of the integration of machine learning with geospatial data. Machine Learning techniques increase the accuracy, efficacy, ...

A map has revealed America’s “epilepsy belt,” highlighting how some states in particular see higher rates of the neurological condition among older adults. The first-of-its kind nationwide study ...