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.

The visual detection and tracking of surface terrain is required for spacecraft to safely land on or navigate within close proximity to celestial objects. Current approaches rely on template matching with pre-gathered patch-based features, which are expensive to obtain and a limiting factor in perceptual capability. While recent literature has focused on in-situ detection methods to enhance navigation and operational autonomy, robust description is still needed. In this work, we explore metric learning as the lightweight feature description mechanism and find that current solutions fail to address inter-class similarity and multi-view observational geometry. We attribute this to the view-unaware attention mechanism and introduce Multi-view Attention Regularizations (MARs) to constrain the channel and spatial attention across multiple feature views, regularizing the what and where of attention focus. We thoroughly analyze many modern metric learning losses with and without MARs and demonstrate improved terrain-feature recognition performance by upwards of 85%. We additionally introduce the Luna-1 dataset, consisting of Moon crater landmarks and reference navigation frames from NASA mission data to support future research in this difficult task. Luna-1 and source code are publicly available at https://droneslab.github.io/mars/.

A Chinese spacecraft carrying rock and soil samples from the far side of the Moon has lifted off from the lunar surface to start its journey back to Earth, according to state media. The achievement on Tuesday is a world first and the latest leap for Beijing's decades-old space programme, which aims to send a crewed mission to the Moon by 2030. The Xinhua News Agency, citing the China National Space Administration (CNSA), said that the ascender of the Chang'e-6 probe took off at 7:38am local time on Tuesday (23:38 GMT) and entered a preset orbit around the moon. It described the move as "an unprecedented feat in human lunar exploration history". The Chang'e-6 probe was launched last month and its lander touched down on the far side of the Moon on Sunday.

The ability to determine the pose of a rover in an inertial frame autonomously is a crucial capability necessary for the next generation of surface rover missions on other planetary bodies. Currently, most on-going rover missions utilize ground-in-the-loop interventions to manually correct for drift in the pose estimate and this human supervision bottlenecks the distance over which rovers can operate autonomously and carry out scientific measurements. In this paper, we present ShadowNav, an autonomous approach for global localization on the Moon with an emphasis on driving in darkness and at nighttime. Our approach uses the leading edge of Lunar craters as landmarks and a particle filtering approach is used to associate detected craters with known ones on an offboard map. We discuss the key design decisions in developing the ShadowNav framework for use with a Lunar rover concept equipped with a stereo camera and an external illumination source. Finally, we demonstrate the efficacy of our proposed approach in both a Lunar simulation environment and on data collected during a field test at Cinder Lakes, Arizona.

America is set to return to the moon on Thursday, marking the first time a US-made craft touched down on the lunar surface since the last Apollo mission in 1972. Odysseus, or Odie, is soaring through space, but unlike previous trips, this one is owned by Houston-based Intuitive Machines. The six-legged robot lander is scheduled to touch down at 6:24pm ET at a crater called Malapert A near the moon's south pole. The landing attempt will be livestreamed on NASA TV beginning at 5pm ET. While the mission is operated by a private company, NASA has sponsored the journey to take its scientific instruments and technology to the moon.

Japan's Slim (Smart Lander for Investigating Moon) mission has now touched on the Moon. If this proves to have been a safe landing, Japan will become only the fifth country to land on the moon. Slim has completed its descent to the lunar surface and we are awaiting confirmation of whether the landing was a success. JAXA, Japan's space agency, expects the landing to take around 20 minutes, with touchdown expected by 15:20 GMT. MailOnline will also be bringing you all the latest updates as the landing progresses, so make sure you check back in!