Artificial intelligence reaches new adoption levels each year. As its adoption becomes more ubiquitous, industries like supply chain management and logistics have begun to leverage AI in innovative ways - taking the front row in the AI show-time. A recent report by Research and Markets "Artificial Intelligence in Supply Chain Management Market" finds that AI in SCM solutions as a whole will reach $15.5B globally by 2026. The large volumes of data generated by these industry verticals, the number of devices employed and the challenges associated with the process require a more defined, elaborate structure to ensure transparency through digital automation. Events such as the unexpected blocking of the world's busiest trade route Suez Canal by a large shipping vessel demonstrate how supply-chain optimization and diversification have become an essential need of the hour.

Upon closer inspection, the satellite had noticed that an area that should have been shrouded in forest, was now barren. Within hours, a call had been made to a global conservation group, who mounted a legal case against the logging companies operating in the area. That process, historically, could have taken months of observing and recording changes. What's more, in remote areas such as the Ussuri Taiga in Russia's Far East, policing illegal logging operations have historically had little impact on the extraction of timber. But thanks to artificial intelligence (AI) and satellites, the ability to observe and respond to changes has become much faster.

Google has pressed forward with its effort to provide artificial intelligence solutions to the Department of Defense, despite an internal employee petition against the company's involvement in a pilot program that analyzes drone footage using AI and the resignations of around a dozen employees who objected to the program. But Google isn't the only company partnering with the Department of Defense on Project Maven--the artificial intelligence pilot program at the heart of the controversy--and the Pentagon has explored the possibility of working with other major tech firms on Project Maven. The involvement of other tech companies in Project Maven makes the project seem more like a bakeoff between several leaders in the field of artificial intelligence and less like a Google-led effort. It also raises questions about whether employees at other companies will raise the same ethical objections to the program that Google employees have. DigitalGlobe, a Colorado-based firm that specializes in geospatial imagery, reportedly provides images and algorithms to Project Maven. IBM has been approached about participating in the project by using artificial intelligence to analyze streaming video, a person familiar with the exchange told Gizmodo.

Advanced machine learning and high-resolution satellite images are set to revolutionise the Australian grape and wine community's regional mapping and vineyard insights. World leading agricultural artificial intelligence software, GAIA (Geospatial Artificial Intelligence for Agriculture), has been developed by Consilium Technology, in partnership with DigitalGlobe and Wine Australia. The software provides groundbreaking insight into the health and quantity of all vineyards across Australia – effortlessly and in real-time. The partnership's initial co-investment will see GAIA deployed in Australia's wine regions to prove that the technology can deliver accurate, timely and cost-effective information about Australia's winegrape vineyards. DigitalGlobe is the world's leading provider of high-resolution Earth imagery.

DigitalGlobe's WorldView 3 satellite captured this image of Sydney, Australia in January 2015. DigitalGlobe has formed a partnership with Ecopia Tech to use proprietary Artificial Intelligence (AI) algorithms and cloud computing to create building footprints. By using Ecopia's U.S. Building Footprints powered by DigitalGlobe, customers will have current information on structures in their areas of interest. Ecopia, a developer in DigitalGlobe's Geospatial Big Data platform (GBDX) ecosystem, established a process to create building footprints quickly and at scale by leveraging machine learning in combination with DigitalGlobe's cloud-based 100 petabyte imagery library. According to Ecopia, the service provides actionable insights for observing, analyzing, and monitoring business processes such as supply chain management, urban planning, and asset monitoring for industries that include energy, insurance, real estate, telecom, and location-based services.

In the 18 years that DigitalGlobe has been operating Earth imaging satellites, we have collected over 100 PB of imagery about our changing planet. For most of that time, this vast amount of data was stored in a series of tape libraries running in our data centers. But in 2015, DigitalGlobe kicked off a project to modernize: moving the entire imagery archive to the cloud. We aimed to optimize our IT costs while serving our customers better by putting our archive where they were already working. In collaboration with Amazon Web Services (AWS), we used AWS Snowmobile, essentially a datacenter in a ruggedized semi-trailer truck, to transfer our archive to Amazon's cloud.

People at DigitalGlobe are fond of saying they've built a "time machine of the planet." And, if we're being metaphorical, that's true: The satellite imaging company has used orbiters to take and store super sharp images of Earth for 17 years. Their eyes have gazed down as trees have disappeared and appeared, floodwaters have flowed in and out, and cities have boomed and busted. But that time-lapse is only useful if someone can make sense of its meaning. Today, that's where companies like Orbital Insight come in, with artificial intelligence that parses the pictures and auto-says things like, "That looks like deforestation," or "There are lots more cars in those parking lots than there were last year.

The Kangbashi district of Ordos, China, looks like a cosmopolitan city of the future. It's just 14 years old but already has all the trappings of a mature municipality. It has a large public library designed to mimic the shape of books on shelves. Elsewhere are a contemporary and cavernous airport, a spectacular-looking stadium, clusters of towering apartment buildings, spacious plazas and parks, a five-story food court with 400 vendors, an intricate opera house, and perfectly paved streets designed to connect more than 300,000 residents to the places they live, work, and play. Although Kangbashi has the appearance of a modern metropolis, the truth is apparent in the one thing it lacks: people. Kangbashi is one of hundreds of "ghost cities" rumored to dot the Chinese countryside. Erected at the height of China's real estate boom, they're pet projects of wealthy local governments that built them to be the center of a virtuous circle: Spending their economic windfalls on megacities, governments believed, would attract inhabitants from outlying agrarian communities, creating new urban centers with which to generate even more wealth.

Hopkinsville, Kentucky, is normally a mid-size town, home to 32,000 people and a big bowling ball manufacturer. But on August 21, its human density more than tripled, as around 100,000 people swarmed toward the total solar eclipse. Hundreds of miles above the crowd, high-resolution satellites stared down, snapping images of the sprawl. These satellites belong to a company called DigitalGlobe, and their cameras are sharp enough to capture a book on a coffee table. And a lot can happen between brunch and dinner.

Th satellite mapping company DigitalGlobe tapped San Francisco design studio Stamen Design to build a machine learning-powered mapping tool that takes income data and satellite imagery to predict average income of city blocks. Called Penny, the program analyzes the shapes, colors, and lines that make up a satellite image. Using corresponding Census data, it looks for patterns between different urban features and income levels. With that information, the algorithm can then guess what the income level is of any given area you point it at–provided it's in the same city the algorithm was trained on. Take, for example, the New York version: Hover over the World Trade Center, and Penny is 86% confident that it's a high-income area; hover over Harlem, and Penny is 99% confident that it's a low-income area.