This past spring, the world experienced a rare moment of collective joy and awe when NASA's Artemis II mission, the first crewed flight to the Moon since 1972, completed its historic lunar flyby. On April 6, astronauts Reid Wiseman, Christina Koch, Victor Glover and Jeremy Hansen flew farther from Earth than any humans before them. Now, as NASA prepares for the next Artemis mission and beyond, the space agency is working with IBM to give scientists new tools to study the Moon. On Thursday, the two organizations released the NASA-IBM Lunar Foundation Model. As a foundation model, it can do a few different things, says Dr. Juan Bernabé-Moreno, the director of IBM Research Europe, UK and Ireland.

NASA is planning to send three small rovers to the Moon with a single instruction: Work out among yourselves how to explore a patch of ground. The Cooperative Autonomous Distributed Robotic Exploration mission, or CADRE, will land on the side of the Moon facing Earth as part of NASA's IM-3 launch, planned for late 2026. These rovers will spend roughly two weeks mapping the terrain as a self-guided team. No joystick will control them, and no human will approve each turn. The rovers will elect a leader among themselves, assign their own tasks and redraw their plans as a group when one of them runs low on charge.

China's new moon mission could unlock secret of lunar ice: Why that matters China is set to launch its Chang'e-7 unmanned, robotic space mission, possibly as early as Monday morning, to look for ice water in the permanently shadowed craters of the moon's south pole. This marks China's seventh and most ambitious moon mission so far. Here is what we know about it. What do we know about Chang'e-7? The Chang'e 7 launch window runs from Monday, August 24 to Monday, August 31, according to launch observers.

The Falcon 9 upper stage created a 60-foot-wide crater on the moon on August 5. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The crater image is enlarged three times from the original, with north facing up, and it covers an area about a quarter of a mile wide. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

The poem appears to have geophysics and geology that was not understood in medieval times. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. "The Divine Comedy" is divided into the "Inferno," "Purgatorio," and "Paradiso." Breakthroughs, discoveries, and DIY tips sent six days a week. Dante Alighieri's is one of the most famous Italian literary works, if not most famous.

Impact craters are among the most prominent geomorphological features on planetary surfaces and are of substantial significance in planetary science research. Their spatial distribution and morphological characteristics provide critical information on planetary surface composition, geological history, and impact processes. In recent years, the rapid advancement of deep learning models has fostered significant interest in automated crater detection. In this paper, we apply advancements in deep learning models for impact crater detection and identification. We use novel models, including Convolutional Neural Networks (CNNs) and variants such as YOLO and ResNet. We present a framework that features a two-stage approach where the first stage features crater identification using simple classic CNN, ResNet-50 and YOLO. In the second stage, our framework employs YOLO-based detection for crater localisation. Therefore, we detect and identify different types of craters and present a summary report with remote sensing data for a selected region. We consider selected regions for craters and identification from Mars and the Moon based on remote sensing data. Our results indicate that YOLO demonstrates the most balanced crater detection performance, while ResNet-50 excels in identifying large craters with high precision. Introduction The automatic detection of craters is a fundamental task in planetary science and has significant implications for geological analysis [1], spacecraft navigation [2], and planetary surface exploration [3]. The identification of craters is essential for spacecraft navigation, identifying hazardous terrains, and exploring planetary resources.

Recent interest in geospatial artificial intelligence (GeoAI) has fostered a wide range of applications using artificial intelligence (AI), especially deep learning, for geospatial problem solving. However, major challenges such as a lack of training data and the neglect of spatial principles and spatial effects in AI model design remain, significantly hindering the in-depth integration of AI with geospatial research. This paper reports our work in developing a deep learning model that enables object detection, particularly of natural features, in a weakly supervised manner. Our work makes three contributions: First, we present a method of object detection using only weak labels. This is achieved by developing a spatially explicit model based on Tobler's first law of geography. Second, we incorporate attention maps into the object detection pipeline and develop a multistage training strategy to improve performance. Third, we apply this model to detect impact craters on Mars, a task that previously required extensive manual effort. The model generalizes to both natural and human-made features on the surfaces of Earth and other planets. This research advances the theoretical and methodological foundations of GeoAI.

As the US prepares to send astronauts back to the moon, a CIA file has resurfaced that claims to have found life there more than 25 years ago. In the 1970s and 80s, the CIA conducted experiments with individuals who claimed they could perceive information about distant objects, events, or people, a process known as'remote viewing.' The experience of remote viewer Ingo Swann was first revealed in 1998 when he explained how his psychic episode took him to the dark side of the moon, a region that always faces away from Earth and out of sight from human eyes. That's where the remote reviewer made a shocking discovery: towers, buildings, and human-like aliens working at a secret complex on the moon's surface. Disturbingly, Swann said government officials knew the aliens had a base there, and these humanoids could actually sense his presence as he viewed them with his mind from 238,000 miles away.

In order to make a pinpoint landing on the Moon, the spacecraft's navigation system must be accurate. To achieve the desired accuracy, navigational drift caused by the inertial sensors must be corrected. One way to correct this drift is to use absolute navigation solutions. In this study, a terrain absolute navigation method to estimate the spacecraft's position and attitude is proposed. This algorithm uses the position of the craters below the spacecraft for estimation. Craters seen by the camera onboard the spacecraft are detected and identified using a crater database known beforehand. In order to focus on estimation algorithms, image processing and crater matching steps are skipped. The accuracy of the algorithm and the effect of the crater number used for estimation are inspected by performing simulations.

This paper provides a theoretical framework for interpreting acoustic neighbor embeddings, which are representations of the phonetic content of variable-width audio or text in a fixed-dimensional embedding space. A probabilistic interpretation of the distances between embeddings is proposed, based on a general quantitative definition of phonetic similarity between words. This provides us a framework for understanding and applying the embeddings in a principled manner. Theoretical and empirical evidence to support an approximation of uniform cluster-wise isotropy are shown, which allows us to reduce the distances to simple Euclidean distances. Four experiments that validate the framework and demonstrate how it can be applied to diverse problems are described. Nearest-neighbor search between audio and text embeddings can give isolated word classification accuracy that is identical to that of finite state transducers (FSTs) for vocabularies as large as 500k. Embedding distances give accuracy with 0.5% point difference compared to phone edit distances in out-of-vocabulary word recovery, as well as producing clustering hierarchies identical to those derived from human listening experiments in English dialect clustering. The theoretical framework also allows us to use the embeddings to predict the expected confusion of device wake-up words. All source code and pretrained models are provided.