The 2010 decade sure had its challenges, but one positive change was the leap in technology capabilities. This holds true not only for consumers, but also for marketers. For example, the proliferation of smartphones with powerful cameras, loads of apps and high-bandwidth mobile networks has changed how we communicate and share ideas. Marketers can promote events on the go, livestream sessions, share pictures and essentially keep people informed -- globally and in real time. In short, we now have a multimedia studio in our hands.
An Australian team is using machine learning to tackle the threat of space junk wrecking new satellites. Research to tackle the growing need to find, capture and remove junk from space is advancing at the Australian Institute for Machine Learning in Adelaide, South Australia. Machine Learning for Space director Tat-Jun Chin and his Adelaide-based team have won a $600,000 grant from Australia's SmartSat CRC to continue their work in detecting, tracking and cataloging space junk. SmartSat CRC was established last year to work with the Australian Space Agency based in Adelaide, contributing to the Australian government's goal of tripling the size of the space sector to $12 billion and creating as many as 20,000 jobs by 2030. The space junk project is based on developing a space-based surveillance network and tackling the growing challenge of crowding in space.
On Christmas Eve, 1968, the astronauts aboard NASA's Apollo 8 spacecraft became the first humans to behold the entirety of Earth with their own eyes. That day, crew member Bill Anders took an iconic photograph called "Earthrise'' that captured our home world emerging from behind the Moon's horizon. "We came all this way to explore the Moon, and the most important thing is that we discovered the Earth," Anders famously said of his mission. More than 50 years later, Earth is being rediscovered from space once again, but this time it is through the "eyes" of satellites, supercomputers, and artificial intelligence (AI) networks. Geospatial science, a sprawling and multifaceted field dedicated to resolving ever-finer details about Earth and its systems, is poised to undergo an unprecedented growth spurt powered by this confluence of technologies across both the public and private sectors. "With the proliferation of satellite platforms, essentially this is something that's almost become impossible to keep a handle on because there are so many new systems being launched and developed by so many different actors globally," said Jonathan Chipman, director of Dartmouth College's Citrin Family GIS/Applied Spatial Analysis Laboratory, in a call. "It's just mind-boggling the amount of data that's now being collected from low-Earth orbit." The feeling of epiphanic connection with the planet experienced by astronauts gazing at Earth is known popularly as "the overview effect," a term coined by author Frank White in his book of the same name. The new geospatial view of Earth, however, may offer something closer to an "overwhelm effect," as our home world is imaged, valued, and monitored by millions of sensors on thousands of spacecraft orbiting Earth. How will we deal with the petabytes of Earth-observation data that may document the collapse of whole ecosystems or the wreckage of natural disasters? What will we do with geospatial information that predicts such dire outcomes but also demands nimble and dramatic changes to our lifestyles? It will take foresight to ensure that the deluge of information is managed in a way that equitably benefits communities and ecosystems around the world, and remains as accessible to the public as possible. "The biggest challenge will be in making sense of all these data," said Dawn Wright, chief scientist of the Environmental Systems Research Institute (Esri), a major geospatial software and data science company, in an email. "It is one thing to store, to distribute, even to analyze, but how do truly understand it?
NASA's ambitious plans to build a base on the surface of the moon will likely be delayed. According to NASA's Dough Loverro, who oversees the agency's human exploration programs, several aspects of the project's technical design and multi-phase rollout need to be revised. One of the first changes will affect NASA's touted Lunar Gateway, a space station planned to orbit the moon and to be used as a staging point for the subsequent construction of a base on the moon's surface. NASA's ambitious plans for a lunar base will be delayed by at least a year after unexpected technical complications with the Lunar Gateway, a space station planned to orbit the moon and used as a staging area for construction materials NASA had targeted a completion window for the Lunar Gateway in 2024, and promised construction on the lunar base would begin no later than 2025, but according to a report in the Wall Street Journal, the Lunar Gateway is being reworked. NASA says it will still have a space station in orbit around the moon in 2024, but it won't initially be as capable as originally planned, likely delaying the completion date for the lunar base.
In terms of raw data, the earth observation industry is undeniably exploding. Investments in freely available data from satellite constellations like MODIS, Landsat, and Sentinel have democratized access to timely satellite imagery of the entire globe (albeit at a lower resolution than you're accustomed to seeing on Google Maps). Meanwhile, cloud providers like AWS and Google Cloud have gone so far as to store satellite data for free, further accelerating global usage of these images. The trouble, naturally, is that interpreting the content of satellite imagery is not an easy task. In the field of remote sensing, researchers have been applying algorithmic techniques to the challenge of earth imagery interpretation for over 70 years.
NASA has equipped its Mars 2020 rover with everything it needs to explore the Red planet, except for a name – until now. Called Perseverance, the rover's title was picked from a'Name the Rover' essay contest that received 28,000 entries from children ranging from kindergartners to high school. The name was revealed on Thursday during a live streaming and was chosen by seventh grader Alex Mathers who's winning essay compared the rover to the human race. 'If you think about it, all of these names of past Mars rovers are qualities we possess as humans.' 'We are always curious, and seek opportunity. We have the spirit and insight to explore the Moon, Mars, and beyond.
NASA's Curiosity rover has shared a stunning panorama of its home. Composed of more than 1,000 images of Mars' landscape taken during the 2019 Thanksgiving holiday, the contains 1.8 billion pixels – deeming it the highest-resolution picture of the Martian planet yet. The rover used its Mast Camera to capture the photos of the Red Planet to produce the high-resolution panorama and relied on its medium-angle lens to for a lower-resolution -nearly 650-million-pixel panorama that includes the rover's deck and robotic arm. NASA's Curiosity rover has shared a stunning panorama of its home. Composed of more than 1,000 images of Mars' landscape taken during the 2019 Thanksgiving holiday, the contains 1.8 billion pixels – deeming it the highest-resolution picture of the Martian planet yet Both panoramas showcase'Glen Torridon,' a region on the side of Mount Sharp that Curiosity is exploring.
Multi-spectral satellite imaging sensors acquire various spectral band images such as red (R), green (G), blue (B), near-infrared (N), etc. Thanks to the unique spectroscopic property of each spectral band with respective to the objects on the ground, multi-spectral satellite imagery can be used for various geological survey applications. Unfortunately, image artifacts from imaging sensor noises often affect the quality of scenes and have negative impacts on the applications of satellite imagery. Recently, deep learning approaches have been extensively explored for the removal of noises in satellite imagery. Most deep learning denoising methods, however, follow a supervised learning scheme, which requires matched noisy image and clean image pairs that are difficult to collect in real situations. In this paper, we propose a novel unsupervised multispectral denoising method for satellite imagery using wavelet subband cycle-consistent adversarial network (WavCycleGAN). The proposed method is based on unsupervised learning scheme using adversarial loss and cycle-consistency loss to overcome the lack of paired data. Moreover, in contrast to the standard image domain cycleGAN, we introduce a wavelet subband domain learning scheme for effective denoising without sacrificing high frequency components such as edges and detail information. Experimental results for the removal of vertical stripe and wave noises in satellite imaging sensors demonstrate that the proposed method effectively removes noises and preserves important high frequency features of satellite images.
NASA is running out of options in its mission to get its InSight lander's probe back on track. According to the agency, it will attempt to use a robotic arm attached to its InSight Lander to push down on a probe meant to drill into Martian soil which has struggled to achieve its mission throughout the past year. NASA says the goal is to stop the probe from popping out of its partially dug hole which it has done twice in recent months in addition to almost burying itself. While the act of pushing down on the probe with the arm should be relatively easy, NASA acknowledges that choosing to do so could create problems for the instrument if too much force is applied. The worry is that pushing down with the arm may damage a ribbon-like stretch of wires that attaches to InSight.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An asteroid hitting Earth is one of humanity's greatest existential threats, making it imperative that asteroid detection is a vital task for government space agencies around the world. Using advanced artificial intelligence, researchers in the Netherlands have discovered several "potentially hazardous objects" that were not spotted by humans. The research, published in Astronomy & Astrophysics, looked at space objects more than 100 meters in diameter that were likely to come within 4.7 million miles of Earth.