Abstract-- We present the POLAR Traverse Dataset: a dataset of high-fidelity stereo pair images of lunar-like terrain under polar lighting conditions designed to simulate a straightline traverse. Images from individual traverses with different camera heights and pitches were recorded at 1 m intervals by moving a suspended stereo bar across a test bed filled with regolith simulant and shaped to mimic lunar south polar terrain. Ground truth geometry and camera position information was also recorded. This dataset is intended for developing and testing software algorithms that rely on stereo or monocular camera images, such as visual odometry, for use in the lunar polar environment, as well as to provide insight into the expected lighting conditions in lunar polar regions. The lunar south polar region is of particular interest to upcoming NASA missions such as the Volatiles Investigating Figure 1: Hardware setup extended over SSERVI test bed with Polar Exploration Rover (VIPER) due to the existence of lunar terrain and lighting.

In this work, we propose a method to learn the solution operators of PDEs defined on varying domains via MIONet, and theoretically justify this method. We first extend the approximation theory of MIONet to further deal with metric spaces, establishing that MIONet can approximate mappings with multiple inputs in metric spaces. Subsequently, we construct a set consisting of some appropriate regions and provide a metric on this set thus make it a metric space, which satisfies the approximation condition of MIONet. Building upon the theoretical foundation, we are able to learn the solution mapping of a PDE with all the parameters varying, including the parameters of the differential operator, the right-hand side term, the boundary condition, as well as the domain. Without loss of generality, we for example perform the experiments for 2-d Poisson equations, where the domains and the right-hand side terms are varying. The results provide insights into the performance of this method across convex polygons, polar regions with smooth boundary, and predictions for different levels of discretization on one task. We also show the additional result of the fully-parameterized case in the appendix for interested readers. Reasonably, we point out that this is a meshless method, hence can be flexibly used as a general solver for a type of PDE.

Global warming is an urgent issue that is generating catastrophic environmental changes, such as the melting of sea ice and glaciers, particularly in the polar regions. The melting pattern and retreat of polar sea ice cover is an essential indicator of global warming. The Sentinel-2 satellite (S2) captures high-resolution optical imagery over the polar regions. This research aims at developing a robust and effective system for classifying polar sea ice as thick or snow-covered, young or thin, or open water using S2 images. A key challenge is the lack of labeled S2 training data to serve as the ground truth. We demonstrate a method with high precision to segment and automatically label the S2 images based on suitably determined color thresholds and employ these auto-labeled data to train a U-Net machine model (a fully convolutional neural network), yielding good classification accuracy. Evaluation results over S2 data from the polar summer season in the Ross Sea region of the Antarctic show that the U-Net model trained on auto-labeled data has an accuracy of 90.18% over the original S2 images, whereas the U-Net model trained on manually labeled data has an accuracy of 91.39%. Filtering out the thin clouds and shadows from the S2 images further improves U-Net's accuracy, respectively, to 98.97% for auto-labeled and 98.40% for manually labeled training datasets.

The moon's polar regions are home to craters and other depressions that never receive sunlight. Today, a group of researchers led by the Max Planck Institute for Solar System Research (MPS) in Germany present the highest-resolution images to date covering 17 such craters. Craters of this type could contain frozen water, making them attractive targets for future lunar missions, and the researchers focused further on relatively small and accessible craters surrounded by gentle slopes. In fact, three of the craters have turned out to lie within the just-announced mission area of NASA's Volatiles Investigating Polar Exploration Rover (VIPER), which is scheduled to touch down on the moon in 2023. Imaging the interior of permanently shadowed craters is difficult, and efforts so far have relied on long exposure times resulting in smearing and lower resolution. By taking advantage of reflected sunlight from nearby hills and a novel image processing method, the researchers have now produced images at 1–2 meters per pixel, which is at or very close to the best capability of the cameras.

NASA says it will put a robotic rover on the moon that can aid the agency in its search for lunar water. The four-wheeled vehicle, which NASA has dubbed the Volatiles Investigating Polar Exploration Rover, or VIPER, will be the size of a golf-cart and use various science instruments to probe the moon's surface for evidence of water and ice. VIPER is set to be delivered to the moon's surface by December 2022 and once there it will collect 100 days worth of data designed to map potential water sources. NASA's VIPER rover (rendering above) will explore the moon for water and help to guide an ongoing plan to return humans to the lunar surface A mobility'testbed' (pictured above) is was created to evaluate the rover's mobility system. It's toolkit for detecting water will include a drill able to bore beneath the surface and a spectrometer than can detect moisture.

Frozen water has been found on the surface of the moon for the first time following the use of high tech satellite scans. Experts say they have detected ice in areas of permanent shadow in the moon's polar regions. They used an imaging technique that can tell different types of water apart, including that on the surface, absorbed into the soil, or bound in minerals. Water has previously been found in the moon's soil, but this is believed to be the first time it has been detected on the surface. Surface water ice was only located in around 3.5 per cent of the moon's shadow covered areas.

These are among the most perilous of environments on planet Earth, places where few humans dare tread. They ain't got nothin', though, on waters of our planet's polar regions, where frigid temperatures and considerable pressures would snuff a puny human like you in a heartbeat. This is the stuff their tough-as-hell bodies were made for. But it comes at a price: Getting the bot back to its icebreaking boat alive can be more challenging than communicating with a Mars rover millions of miles away. Seabed doesn't swim like your typical autonomous underwater vehicle.

MIAMI – In a move that shocked lunar scientists, NASA has cancelled the only robotic vehicle under development to explore the surface of the moon, despite President Donald Trump's vow to return people there. Scientists working on the Resource Prospector (RP) mission, a robotic rover that had been in development for about a decade to explore a polar region of the moon, expressed astonishment at the decision. "We now understand RP was cancelled on 23 April 2018 and the project has been asked to close down by the end of May," said the letter, dated April 26, by the Lunar Exploration Analysis Group. It was addressed to NASA chief Jim Bridenstine and posted on the website NASAWatch.com. "This action is viewed with both incredulity and dismay by our community," particularly because Trump's space policy "directs NASA to go to the lunar surface," the letter said.

The State Government of South Australia announced their contract with Solar Reserve to build a 150MW solar thermal power plant for Port Augusta, South Australia. This is an addition to the state-owned gas plant and the world's largest lithium ion battery recently announced contract with Tesla. According to State Premier Jay Weatherhill, this solar thermal plant "biggest of its kind in the world" and "will help make our energy grid more secure." This Aurora Solar Energy Project will be ready in 2020 and is expected to supply 100% of the government's anticipated power needs. IntelAI has been collaborating with NASA FDL's Lunar Water and Volatiles team in a 9-week program this year. Working with Intel's team and their deep learning technologies, Intel Nervana, NASA is looking to accelerate the development of a software solution to take AI to the moon.

Everything is more extreme on the moon. On top of temperatures that range from -300 F to 224 F, future astronauts and probes must deal with lighting conditions generously described as "harsh." To help, researchers at Ames Research Center in Silicon Valley created a lunar testbed, complete with craters, fluffy dust and solar simulator lights. The goal is to develop sensors that can "see" in such conditions to help probes and, eventually, humans navigate the surface safely. With no atmosphere to scatter and reflect lighting, "what you get on the Moon are dark shadows and very bright regions that are directly illuminated by the Sun -- the Italian painters in the Baroque period called it chiaroscuro," says NASA Ames computer scientist Uland Wong.