--Classifying potentially hazardous asteroids (PHAs) is crucial for planetary defense and deep space navigation, yet traditional methods often overlook the dynamical relationships among asteroids. We introduce a Graph Neural Network (GNN) approach that models asteroids as nodes with orbital and physical features, connected by edges representing their similarities, using a NASA dataset of 958,524 records. Despite an extreme class imbalance with only 0.22% of the dataset with hazardous label, our model achieves an overall accuracy of 99% and an AUC of 0.99, with a recall of 78% and an F1-score of 37% for hazardous asteroids after applying Synthetic Minority Oversampling T echnique. Feature importance analysis highlights albedo, perihelion distance, and semi-major axis as main predictors. This framework supports planetary defense missions and confirm AI's potential in enabling autonomous navigation for future missions such as NASA's NEO Surveyor and ESA's Ramses, offering an interpretable and scalable solution for asteroid hazard assessment. However, a small subset known as potentially hazardous asteroids (PHAs) follow orbits that bring them perilously close to our planet, raising the specter of catastrophic collisions. Historical events, such as the 1908 Tunguska explosion [1], which devastated over 2,000 square kilometers of Siberian forest, and the 2013 Chelyabinsk meteor [2], which injured over 1,000 people and caused widespread property damage, show the destructive potential of these celestial bodies.

NASA's Frontier Development Lab (FDL), a public-private research institute operated jointly by the space agency's Ames Research Center and the SETI Institute, announced it will use artificial intelligence to study methods of protecting the Earth from potentially hazardous asteroids and comets. The announcement was made on Friday, June 30, designated in 2014 as International Asteroid Day, an annual event that addresses potential threats from Near Earth Objects (NEOs). June 30 was chosen because it is the anniversary of the 1908 Tunguska impact, when an asteroid estimated to have been 120 feet wide exploded over the Stony Tunguska River in Siberia. The annual commemoration is the brainchild of astrophysicist and Queen lead guitarist Brian May and film director Grigorij Richters. Several years ago, Richters directed 51 Degrees North, a film depicting a fictional asteroid strike in London. For this year's event, FDL assembled a research team to discuss the ways artificial intelligence can assist in planetary defense.

Hosted by the SETI Institute, NASA Frontier Development Lab (FDL) is an AI R&D accelerator that teams planetary and data scientists, adds leading edge technical resources and skills from the private sector and tackles knowledge gaps useful to the space program. FDL teams address tightly defined problems and its format encourages rapid iteration and prototyping to create outputs that have meaningful application. In 2016, three teams developed breakthrough solutions in meteor discovery, shape modeling and deflection scenarios. Each team included two data scientists and two planetary astronomers, and were mentored by SETI Institute scientists Michael Busch, Peter Jenniskens and Franck Marchis. To deepen and diversify each group's scientific and technological work, experts from the fields of planetary science, machine learning and deep thinking, drone technology, space-based technology, and space-mission design and operation were also asked to guide the three groups.