After weeks of silence, NASA has officially dismissed speculation that 3I/ATLAS has anything to do with aliens. After the temporary shutdown of the US government, NASA has finally started its nonessential work back up. It's starting off with a bang: The agency called a press conference to show its hitherto reserved images of the interstellar object 3I/ATLAS. NASA scientists also confirmed that 3I/ATLAS is in fact a comet, contrary to the speculations about alien technology flooding the internet. During the broadcast, a panel of scientists showed the results of observations obtained by different NASA missions across various points in the journey 3I/ATLAS has taken .

This work presents a new dataset for the Martian digital elevation model prediction task, ready for machine learning applications called MCTED. The dataset has been generated using a comprehensive pipeline designed to process high-resolution Mars orthoimage and DEM pairs from Day et al., yielding a dataset consisting of 80,898 data samples. The source images are data gathered by the Mars Reconnaissance Orbiter using the CTX instrument, providing a very diverse and comprehensive coverage of the Martian surface. Given the complexity of the processing pipelines used in large-scale DEMs, there are often artefacts and missing data points in the original data, for which we developed tools to solve or mitigate their impact. We divide the processed samples into training and validation splits, ensuring samples in both splits cover no mutual areas to avoid data leakage. Every sample in the dataset is represented by the optical image patch, DEM patch, and two mask patches, indicating values that were originally missing or were altered by us. This allows future users of the dataset to handle altered elevation regions as they please. We provide statistical insights of the generated dataset, including the spatial distribution of samples, the distributions of elevation values, slopes and more. Finally, we train a small U-Net architecture on the MCTED dataset and compare its performance to a monocular depth estimation foundation model, DepthAnythingV2, on the task of elevation prediction. We find that even a very small architecture trained on this dataset specifically, beats a zero-shot performance of a depth estimation foundation model like DepthAnythingV2. We make the dataset and code used for its generation completely open source in public repositories.

A strange formation that resembles a bear's face was captured on the surface of the Red Planet by NASA's Mars Reconnaissance Orbiter last month. Two perfectly placed craters make up the eyes, a hill with a "V-shaped collapse structure" makes up the nose, and a circular fracture pattern forms the head, according to the University of Arizona's Lunar and Planetary Laboratory, which controls the orbiter's camera. "The circular fracture pattern might be due to the settling of a deposit over a buried impact crater," the lab explained. "Maybe the nose is a volcanic or mud vent and the deposit could be lava or mud flows?" The University of Arizona released this photo of a formation on the surface of Mars that resembles a bear's face.

Winter on Mars transforms the Red Planet into something spectacular, but it's not quite like like a Hallmark greeting card's holiday scene. Temperatures at the planet's poles plummet to bone-chilling lows of minus 190 degrees Fahrenheit. Although humans are years from colonizing Mars, NASA's robotic rovers on the planet reveal a few discoveries about the colder season. The HiRISE camera aboard NASA's Mars Reconnaissance Orbiter captured these images of sand dunes covered by frost just after winter solstice The Mars Reconnaissance Orbiter has been in orbit for more than 16 years and has returned over 436 terabits of data back to NASA. Mars is the fourth planet from the sun, with a'near-dead' dusty, cold, desert world with a very thin atmosphere.

For the past 15 years, NASA's Mars Reconnaissance Orbiter has been doing laps around the Red Planet studying its climate and geology. Each day, the orbiter sends back a treasure trove of images and other sensor data that NASA scientists have used to scout for safe landing sites for rovers and to understand the distribution of water ice on the planet. Of particular interest to scientists are the orbiter's crater photos, which can provide a window into the planet's deep history. NASA engineers are still working on a mission to return samples from Mars; without the rocks that will help them calibrate remote satellite data with conditions on the surface, they must do a lot of educated guesswork when it comes to determining each crater's age and composition. For now, they need other ways to tease out that information.

The Mars Reconnaissance Orbiter's HiRISE camera captured this impact crater on Mars. On July 15, 1965, the Mariner 4 spacecraft snapped a series of photographs of Mars during its flyby of the Red Planet. These were the first "close-up" images taken of another planet from outer space, according to NASA. One of these first grainy photographs depicted a massive crater nearly 100 miles in diameter. Now, NASA's Jet Propulsion Laboratory (JPL) is tapping artificial intelligence (AI) to help with its cosmic cartography efforts, using these technologies to identify "fresh craters" on Mars.

An innovative artificial intelligence (AI) tool developed by NASA has helped identify a cluster of craters on Mars that formed within the last decade.The new machine-learning algorithm, an automated fresh impact crater classifier, was created by researchers at NASA's Jet Propulsion Laboratory (JPL) in California -- and represents the first time artificial intelligence has been used to identify previously unknown craters on the Red Planet, according to a statement from NASA. Scientists have fed the algorithm more than 112,000 images taken by the Context Camera on NASA's Mars Reconnaissance Orbiter (MRO). The program is designed to scan the photos for changes to Martian surface features that are indicative of new craters. In the case of the algorithm's first batch of finds, scientists think these craters formed from a meteor impact between March 2010 and May 2012. Related: Latest photos from NASA's Mars Reconnaissance Orbiter"AI can't do the kind of skilled analysis a scientist can," Kiri Wagstaff, JPL computer scientist, said in the statement.

An innovative artificial intelligence (AI) tool developed by NASA has helped identify a cluster of craters on Mars that formed within the last decade. The new machine-learning algorithm, an automated fresh impact crater classifier, was created by researchers at NASA's Jet Propulsion Laboratory (JPL) in California -- and represents the first time artificial intelligence has been used to identify previously unknown craters on the Red Planet, according to a statement from NASA. Scientists have fed the algorithm more than 112,000 images taken by the Context Camera on NASA's Mars Reconnaissance Orbiter (MRO). The program is designed to scan the photos for changes to Martian surface features that are indicative of new craters. In the case of the algorithm's first batch of finds, scientists think these craters formed from a meteor impact between March 2010 and May 2012.

The High-Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter took this image of a crater cluster on Mars, the first ever to be discovered by artificial intelligence (AI). NASA said, "These craters were created by several pieces of a single meteor. The largest of the craters is about 13 feet (4 meters) wide. In total, the craters span about 100 feet (30 meters) of the red planet's surface. The craters were found in a region called Noctis Fossae, located at latitude -3.213, longitude 259.415."

Sometime between March 2010 and May 2012, a meteor streaked across the Martian sky and broke into pieces, slamming into the planet's surface. The resulting craters were relatively small - just 13 feet (4 meters) in diameter. The smaller the features, the more difficult they are to spot using Mars orbiters. But in this case - and for the first time - scientists spotted them with a little extra help: artificial intelligence (AI). It's a milestone for planetary scientists and AI researchers at NASA's Jet Propulsion Laboratory in Southern California, who worked together to develop the machine-learning tool that helped make the discovery.