Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

AI-driven methods have potential to minimise manual labour during planetary data processing and aid ongoing missions with real-time data analysis. This white paper focuses on key areas of AI-driven research, the need for open source training data, and the importance of collaboration between academia and industries to advance AI-driven research

The Mercury Radiometer and Thermal Infrared Spectrometer (MERTIS) at the Moon First Results and Status Report

In October 2020, BepiColombo performed a flyby maneuver at the Moon. The Mercury Radiometer and Thermal Infrared Spectrometer (MERTIS) was among the few instruments that could observe the Moon. We report on first preliminary results

BepiColombo – Correction of MERTIS Geometry

Based on lunar observations, we calibrated the observation geometry of the MERTIS instrument on board the BepiColombo spacecraft

Studying the Composition and Mineralogy of the Hermean Surface with the Mercury Radiometer and Thermal Infrared Spectrometer (MERTIS) for the BepiColombo Mission: An Update

Launched onboard the BepiColombo Mercury Planetary Orbiter (MPO) in October 2018, the Mercury Radiometer and Thermal Infrared Spectrometer (MERTIS) is on its way to planet Mercury. MERTIS consists of a push-broom IR-spectrometer (TIS) and a radiometer (TIR), which operate in the wavelength regions of 7-14 μm and 7-40 μm, respectively. This wavelength region is characterized by several diagnostic spectral signatures: the Christiansen feature (CF), Reststrahlen bands (RB), and the Transparency feature (TF), which will allow us to identify and map rock-forming silicates, sulfides as well as other minerals. Thus, the instrument is particularly well-suited to study the mineralogy and composition of the hermean surface at a spatial resolution of about 500 m globally and better than 500 m for approximately 5-10% of the surface. The instrument is fully functional onboard the BepiColombo spacecraft and exceeds all requirements (e.g., mass, power, performance). To prepare for the science phase at Mercury, the team developed an innovative operations plan to maximize the scientific output while at the same time saving spacecraft resources (e.g., data downlink). The upcoming fly-bys will be excellent opportunities to further test and adapt our software and operational procedures. In summary, the team is undertaking action at multiple levels, including performing a comprehensive suite of spectroscopic measurements in our laboratories on relevant analog materials, performing extensive spectral modeling, examining space weathering effects, and modeling the thermal behavior of the hermean surface

Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

Over the past decades of NASA’s inner solar system exploration, data obtained from the Moon alone accounts for ~76%. Most of the lunar orbital spacecraft of the past and present carried imaging cameras and spectrometers (including multispectral and hyperspectral payloads), as well as a large variety of other passive and active instruments. For example, NASA’s Lunar Reconnaissance Orbiter (LRO) has been operating for more than 10 years, providing us with ~1206 TB of lunar data which amounts to ~99.5% of the total data contributed by NASA built instruments. Given recent advances in instrument and communication capabilities, the amount of data returned from spacecraft is expected to keep rising quickly. The white paper focus on potential components of AI and ML that could help to accelerate the future exploration of the Moon and other planetary bodies. The white paper highlights on selected AI/ML-based approaches for lunar and planetary surface science and exploration, the need for open-source availability of training, validation, and testing datasets for AI-ML based approaches, and need for opportunities to further bridge the gap between industry and academia for advancing AI-ML based research in lunar and planetary science and exploration