Today’s Earth scientists are spending less time standing in fields collecting soil samples, and more time behind a computer screen. Most geoscience data is automatically collected by sensors and satellites. The big challenge is making sense of all that data so that scientists can get back to what they do best: Observing the world, asking questions, conducting experiments, and finding evidence.

Scientists use large, publicly available datasets from government programs such as NASA, NOAA, and USGS (that’s the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and US Geological Survey, in non-acronym speak). Many Earth scientists also have private sources, and combining these public and private datasets is difficult and time-consuming.

If a scientist wants to look at satellite images to gain a better understanding of climate change, for example, they have to spend hours sifting through data and managing several software programs.

“You want to reduce the time that you’re just managing data and get to those real meaty scientific questions,” says Dr. Annie Burgess. She is the lab director at the Earth Science Information Partners (ESIP) Lab, which funded a project led by Dr. Ziheng Sun, Principal Investigator, Center for Spatial Information Science and Systems at George Mason University. He developed Geoweaver, a program that solves the big data challenges that earth scientists face.

Sun developed a web-based system for deep learning on multiple datasets. It provides geoscientists with a system for making sense of public data (such as satellite images from NASA and NOAA) and private data (such as field observations). The project, called Geoweaver, helps earth scientists effectively use machine learning to sift through data so they can understand what’s really going on with our planet.

“ESIP Lab Geoweaver is an online application for scientists to manage their research workflows,” Sun explains. “It could be installed anywhere and accessed from anywhere. It is a life-saving project for people coding in multiple languages, dealing with multiple facilities and multiple datasets to carry out their science workflows.” 

Machine learning isn’t new, but previous versions were too slow to support the real-time data that Earth scientists need. Today’s computational power is much better, so Sun’s program can train on field data in much less time. He says the old, slow versions didn’t work, so geoscientists don’t have faith in machine learning. That’s why he created a program that combines the newest AI techniques with the programs that they already know and trust.

“Geoweaver will accelerate the adoption of artificial intelligence techniques in science,” Sun says. “It allows scientists to combine their legacy programs and datasets with the cutting-edge deep learning algorithms to create AI models which can more accurately and more automatically understand and predict our environment.”

His research group is already using the program in the lab every day for traditional geoscience research, such as studying crop yield prediction, agricultural drought, flooding damage assessment, and air quality prediction.

This research is more important now than ever because traditional models for agricultural markers such as crop yield didn’t factor in the rising global temperatures.

Burgess explains, “In a time of change in climate, really understanding something like crop yield, which affects the economy, affects global food supply.” She adds, “As the climate is more variable, you can’t rely on standard modeling techniques. And so the type of work that Ziheng Sun is doing where you’re using machine learning and satellite imagery, it’s going to prove more robust output for the future in a time of changing climate.”

That’s why her lab (ESIP) is providing small grants to help scientists like Dr. Sun develop prototypes that combine classic science techniques with the advantages of big data.

Geoweaver was designed with geoscientists in mind, but it can also be used by other scientists or even people working with data in completely different disciplines. It’s for people who are managing multiple servers and data sets for a machine learning workflow.

“It can be used by anybody who deals with servers, deals with multiple servers, multiple end features, and multiple operating systems,” Sun says. He is using the program in his lab now, and developing the final version of the open-source software is expected to be available in the next six months. 

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Huawei has unveiled its new smart cities digital platform utilising artificial intelligence (AI) and Internet of Things (IoT) capabilities, which it said could be used across smart public safety, environmental protection, transportation, government, education, and agriculture.

Huawei’s +AI Digital Platform connects what it calls the brain or command centre; the central nervous system, or network; and the peripheral nervous system, made up of sensors across a city.

“Just like an operating system, the platform is compatible with different city sensors, creates a city digital twin, and supports diverse city applications,” Huawei Enterprise Business Group VP Ma Yue said.

The smart cities digital platform combines AI, IoT, big data, a geographic information system, video, cloud, converged communications, and security.

“Huawei has also developed a middleware platform to provide services to software application partners. This is designed to help application partners quickly develop upper-layer applications to accelerate transformation and innovation in city management, city services, and industry development,” the Chinese networking giant added.

According to Huawei, smart cities solutions are now in their fourth stage thanks to the addition of greater AI capabilities.

“A smart city development race driven by the growing global digital economy is taking place around the world,” Huawei said.

“Smart city adoption has undergone the first stage of breaking down data silos; the second stage of the rise of mobile internet applications; and the third stage of IoT deployment for a collection of mass volumes of city data.

“It is now at the fourth stage, where cities are improving their management capabilities through AI-enabled data mining, achieving the integration of digital technologies and city governance to promote sustainable city development.”

Huawei said its smart cities solution has now been deployed across more than 160 cities in 40 nations, including in Duisburg, Germany; Rustenburg, South Africa; and Tianjin’s Binhai New Area.

Huawei last month unveiled its AI strategy and portfolio, including a series of chips, cloud services, and products.

The company’s Ascend AI chip series includes the Ascend 910 and Ascend 310, with the company also unveiling the Compute Architecture for Neural Networks (CANN), a chip operators library and automated operators development toolkit, and MindSpore, a device, edge, and cloud training and inference framework.

The latter includes “full-pipeline services, hierarchical APIs, and pre-integrated solutions”, Huawei said, with the Chinese networking giant to later expand its AI stack to include an AI acceleration card, AI server, AI appliance, and other AI products.

“Huawei’s AI strategy is to invest in basic research and talent development, build a full-stack, all-scenario AI portfolio, and foster an open global ecosystem,” rotating chair Eric Xu said in October.

Huawei at the same time announced its smart cities AI partnership with Tianjin Binhai New Area, as well as a smart campus solution and joint innovation laboratories alongside Chinese real estate developer Vanke.

“The Tianjin Economic-Technological Development Area designed and developed an AI-based ‘1 + 4 + N’ smart city solution, which refers to one centre, four platforms, and additional innovative applications,” Huawei said.

The centre is Huawei’s “city brain” Intelligent Operations Centre, which aggregates and processes data collected from the government, businesses, and citizens through IoT applications and internet access.

The four AI platforms are then Resident Voices, which has voice recognition for all citizens of Tianjin; Sensing the City, which uses image recognition across people, places, vehicles, and things “for the purpose of fostering harmony for all”; Resident Care, which involves deep learning and correlation for personalised services for citizens; and Enterprise Services, which ensures services availability match their need by applying “multi-dimensional and correlation analysis to clarify the internal relationships of industries in the TEDA district”.

Huawei’s AI push saw it sign a strategic agreement with Chinese search engine giant Baidu in December last year to build an open mobile AI ecosystem that covers platforms, technology, internet services, and content ecosystems.

Huawei head of Consumer Software Engineering and director of Intelligence Engineering Felix Zhang had last year said the addition of AI capabilities to smartphones will bring the next shift in technology.

Huawei had unveiled its Kirin 970 chipset with built-in AI in September 2017, at the time calling it the “future of smartphones”. Its mobile AI is made up of a combination of on-device AI and cloud AI.

Huawei had in May also announced the launch of its eLTE Multimedia Critical Communications System (eLTE MCCS), which it said provides “ultra-reliable” communications solutions for public safety organisations.

According to Huawei, the narrowband networks traditionally used for public safety are limited to providing access to basic voice services. The eLTE MCCS service uses a mobile service convergence platform to interconnect such networks with video surveillance and GIS.

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While the term ‘Smart City’ is a commonly used one, the idea which the term refers to is one that lacks a universally accepted definition. A smart city is understood to be a municipality that leverages new technologies and networks to create a core infrastructure that helps coordinate and optimize systems.  The concept of smart cities overall promises a higher standard of life. The deployment of technologies like Artificial Intelligence, IoT, Robotics, and Big Data can help to create a city that constantly collects and runs on data and data-driven systems. It uses this data to constantly strive to resolve the problems that cities face today – congestion, lack of access to services, crime, and sustainable development, amongst others.

However, any data-driven network has to contemplate its vulnerability to external cyber-attacks, and the hacking of a smart city would be catastrophic. Where smart cities could use network cameras or IoT devices to identify crime and track criminals, collect traffic, data and images, hackers could take advantages on vulnerability and trigger massive cyber-attacks. Robots could be useful for working in sanitation, but hackers could create utter chaos with an army of automatons rising up from the sewers. At the most basic and fundamental level, a smart city will be collecting massive amounts of data about millions of human lives – data that could detail their movements, preferences, activities, and personal information. It would constitute a massive breach of privacy if it fell into the wrong hands.

The increasing amounts of information that a smartphone can collect about your life can induce paranoia, but there are solutions – switching the phone off, disconnecting it from the internet, installing the right software and taking the right precautions, for instance. When the basic infrastructure of the city around you is collecting your information, what can you do? What recourse is available at an individual level? Of course, this information is useful for authorities at a macro-level to design and construct efficient systems around you. That still means that every moment that you are in the city, there is some sensor or some system in your vicinity which at that very moment is collecting important information about you.

To ensure that the meaningful benefits that smart cities can bring to urban living are not lost to the fear of cyber attackers will require a robust and comprehensive cyber security system. A combination of hardware and software systems will ensure security of the main data repository, which individual systems – IoT sensors, CCTV cameras, and so forth – will have to be designed in a manner to prevent misuse and hacking. Users are also encouraged to set up strong password in personal IoT device and safeguard the button line together with device provider. EY called cyber security a ‘necessary pillar’ of smart cities in a recent report, and suggested end-to-end encryption, strong access controls, and an isolation of trusted resources from public resources.

Smart cities are a huge opportunity for Indians to access a brighter future with better transportation, waste management, energy management, and a comprehensively improved standard of living. However, smart city models should boost development while not compromising on data privacy and security. These cities will be built by a diverse ecosystem of providers, and care should be exercised to select the most secure cutting-edge technology products that shall be at the heart of India’s smart city program.

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