What is the future of Big Data, Automation & Robotics, IoT and Cloud in Geospatial Industry?

Cloud is expected to have higher growth than Bigdata, IoT or Automation in the geospatial industry by 2020.

Understanding the extensive technology advancement happening around in all the industries is essential for a business to sustain. The innovation revolves mostly around IoT, Bigdata, Cloud and Robotics which shapes up the coming future. Although Bigdata, IoT, Cloud and Automation & Robotics are very much hyped already we need to understand why it drives the geospatial industry. And what is the future of these technologies and how will these technologies shape up in the coming years? The answer to the questions is encompassed in this blog.

The driving factors and key to future of geospatial industry needs to be analysed and this information is very much crucial to sustain in the coming years. The existing businesses need to grow up to the next level in the value chain as the processes are becoming more automatic and business direction is changing dynamically.

The Global Geospatial Industry Outlook, an exclusive study by Geospatial Media and Communications studies the technologies driving geospatial industry. The report explains how geospatial technology converges with Bigdata, Cloud, IoT and Automation and discusses the changing geospatial technology landscape. The study also ranks 50 economies on the basis of their geospatial preparedness.

Cloud

Cloud enables people to access any information from anywhere in the world. Everything is connected. All the information is available online and data transfer or the communication also has to happen which is brought about through “Cloud”. As per the research, future of cloud is growing steadily. Cloud ensures better delivery of the geospatial solutions.  The adoption of open source software, spatial analytics tools, etc. in the geospatial community is constantly increasing recently and this is driven by the enhanced infrastructure brought about by cloud. As the number of users of cloud in geospatial industry has increased and in future also it is projected to increase steadily to have a 70% adoption level. Almost three fourth of the business is forecasted to shift with the intervention of cloud. This indeed is a big transition, so the business community which is still delivering software through offline mode should direct their resources towards the cloud adoption.

IoT

IoT uses cloud to deliver the solutions. All the resources are in a connected environment and the resources uses the cloud platform to deliver the solutions. Between the years of 2012 and 2016, the market of IoT has seen a constant growth and in future 60% of geospatial solutions will have the convention of IoT. The driving technologies of today will be conventional technologies of tomorrow. By 2020, there are 50Bn devices are expected to be connected. So there is a constant growth in the market and the future is also projected to have a heavy increase.

Big Data

Big data in the geospatial industry came in the year of 2012 . The intrusion of Bigdata in to the geospatial industry was less than 10% in 2012, which saw a steady growth in the consecutive years. As the operations started becoming more and more complex there were a lot of data being generated and all these data need to be analysed through some means and technique. Big Data helped in crunching the complex data into simple and understandable results. Further over the 2015-16, Bigdata saw steady increase in adoption within geospatial industry. The future projection of Bigdata is that it is seen to grow further nearly up to 50% of the adoption back in 2012.

Automation & Robotics

As the process is becoming more and more automatic the human intervention is reducing day by day. The projection of automation & robotics is rising towards 2020 is less than Bigdata, Cloud and IoT. There is a slight increase between the years 2012 and 2013 and a sudden drop towards 2014. In 2015, the Automation & Robotics industry is revamping and growing further towards 2020. In 2016, it has the highest adoption about of 15% and is expected to be nearly 40% in 2020. This substantiates the growing adoption of the automation & robotics in the geospatial industry.

All our “The hyped technologies” of IoT, BigData, Cloud and Automation & Robotics are driving the geospatial technology currently and expected to drive the industry in future as well but at a much bigger pace and at a larger level. The future of geospatial technology is destined by the driving technologies of IoT, Bigdata, Cloud, and Automation & Robotics.

(Source: Geospatial World)

 

Role of IoT in enabling smart cities

As there is a lot of talk going around  smart cities, it was only natural that IoT or Internet of Things, which is considered to be an important component for smart cities across the world, to be integrated in the government’s flagship smart cities mission project, to make them more intelligent and self-reliant. And today, we are seeing that IoT has become an integral part of smart cities, so much so that for each one of our needs, the developers want to offer an IoT solution.

Role of geospatial technologies in IoT

Now drawing the connection between geospatial technologies and IoT, this study explains that in a network of billions of connected devices, to identify and operate one device remotely would be very challenging unless the device knows its geographical location on Earth. Every element that will be connected in this network must identify their unique identification, location and functionalities in order to function properly.

How IoT is making a difference?

The concept of IoT is being developed to make the internet even more immersive and pervasive. So that a wide variety of devices such as, home appliances, surveillance cameras, monitoring sensors, actuators, displays, vehicles, and so on, can be fostered under one network of connected devices.

Employing IoT will enable the development of a number of applications that make use of potentially enormous amount and variety of data generated by such objects to provide new services to citizens, companies, and public administrations. For instance, when we talk about smart cities, we don’t think of cities that have the word – smart, only in their name, instead, we think of cities that work from to their own intelligence.

For a better understanding of how it works, let’s say that you live in a smart city and you are planning to go out for dinner. But first, you want to buy that dress you were checking out last week at the mall near you. So you pick up your phone and you log on to that fashion store’s website and you choose the dress, select the size, pay for the bill, and order the dress to be delivered to your home. The website’s server immediately takes a note of your request and sends an alert to the store-staff about the order. The staff immediately processes the order and sends out a drone to deliver your package. Then the UAV flying across the city, lands in your apartment’s balcony and drops the package and returns to its owner. Immediately, you get a notification on your phone saying that the package has been delivered. And you go check in balcony where it’s waiting for you. Similarly, there can be numerous example where the devices are connected to each other, working in synergy to facilitate your needs.

The US city, Denver is a better example of this. Located south of Denver International Airport, the futuristic neighborhood of Pena Station Next began getting smart LED street lights last month. The technological implementation of IoT in Denver is being planned by Panasonic, where the company has covered the parking with solar panels, plus a storage microgrid is almost done. The area is being developed for the future’s autonomous shuttles to transport residents to the nearby RTD rail stop, shops and restaurants.

Getting developed by Panasonic, the city – Denver was chosen to create a smart city lab and test different technologies. Click here to see some of the future technologies that may show up in Pena Station Next.

The future of IoT

According to a report, many industry experts and excited consumers have pegged the IoT as the next industrial revolution. Whereas, according to some, the power of IoT is applied to more technology use cases – keep it grounded and real. Now it’s a data first world long-term as we collect the data and determine what it can tell us about interoperability, cross correlation benefits. The web of devices is correlating – triangulating disparate data to obtain unforeseen insights.

Based on an analysis done by management consulting firm, McKinsey, the total market size of IoT that was up to $900M in 2015, is expected to rise by $3.7B in 2020 attaining a 32.6% CAGR.

(Source: Geospatial World)

What is the difference between AI, machine learning and deep learning?

You can think of artificial intelligence (AI), machine learning and deep learning as a set of a matryoshka doll, also known as a Russian nesting doll. Deep learning is a subset of machine learning, which is a subset of AI.

Artificial intelligence is any computer program that does something smart. It can be a pile of if-then statements or a complex statistical model. AI can refer to anything from a computer program playing a game of chess, to a voice-recognition system like Amazon’s Alexa interpreting and responding to speech. The technology can broadly be categorized into three groups — Narrow AI, artificial general intelligence (AGI), and superintelligent AI.

IBM’s Deep Blue, which beat chess grandmaster Garry Kasparov at the game in 1996, or Google DeepMind’s AlphaGo, which in 2016 beat Lee Sedol at Go, are examples of narrow AI — AI that is skilled at one specific task. This is different from artificial general intelligence (AGI), which is AI that is considered human-level and can perform a range of tasks. Superintelligent AI takes things a step further. As Nick Bostrom describes it, this is “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom, and social skills.” In other words, it is when the machines have outfoxed us.

Machine learning is a subset of AI. The theory is simple, machines take data and ‘learn’ for themselves. It is currently the most promising tool in the AI kit for businesses. Machine learning systems can quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. Unlike hand-coding a software program with specific instructions to complete a task, machine learning allows a system to learn to recognize patterns on its own and make predictions.

While Deep Blue and DeepMind are both types of AI, Deep Blue was rule-based, dependent on programming — so it was not a form of machine learning. DeepMind, on the other hand — beat the world champion in Go by training itself on a large data set of expert moves.

That is, all machine learning counts as AI, but not all AI counts as machine learning.

Deep learning is a subset of machine learning. Deep artificial neural networks are a set of algorithms setting new records in accuracy for many important problems, such as image recognition, sound recognition, recommender systems, etc.

It uses some machine learning techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be expensive and requires massive datasets to train itself on. That’s because there are a huge number of parameters that need to be understood by a learning algorithm, which can initially produce a lot of false-positives. For instance, a deep learning algorithm could be instructed to ‘learn’ what a dog looks like. It would take a very massive dataset of images for it to understand the minor details that distinguish a dog from a wolf or a fox.

Deep learning is part of DeepMind’s notorious AlphaGo algorithm, which beat the former world champion Lee Sedol in 4 out of 5 games of Go using deep learning in early 2016. The way the deep learning system worked was by combining “Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play,” according to Google.

(source: Geospatial World)

 

Chúc mừng sinh viên K58 tại FIMO bảo vệ thành công khóa luận tốt nghiệp

Ngày 16/5/2017, lễ bảo vệ khóa luận tốt nghiệp cho sinh viên K58 đã được tổ chức tại Đại học Công Nghệ, Đại học Quốc gia Hà Nội.

Năm nay, trung tâm FIMO có 13 sinh viên đăng ký bảo vệ trong đợt 1/2017.

Chúc mừng các sinh viên FIMO đã bảo vệ thành công khóa luận tốt nghiệp với kết quả cao. Đặc biệt, hai bạn Lưu Quang Thắng và Hà Đức Văn đều đạt được điểm bảo vệ cao nhất trong các hội đồng.

Danh sách sinh viên và đề tài bảo vệ năm nay:

STT Họ tên Tên đề tài
1 Hà Đức Văn Nghiên cứu và chế tạo thiết bị Fair Box sử dụng cảm biến giá rẻ cho hệ thống giám sát nồng độ bụi (PM)
2 Phạm Văn Chính Ứng dụng bộ chuẩn OGC SWE và phần mềm mã nguồn mở 52o North trong việc xây dựng FAirWeb cho hệ thống FAirNet giám sát nồng độ bụi (PM)
3 Phạm Mạnh Cường Xây dựng và phát triển ứng dụng FairApp cho hệ thống FairNet giám sát nồng độ bụi (PM)
4 Ngô Khắc Thành Ứng dụng bộ chuẩn OGC SWE và phần mềm mã nguồn mở 52o North trong việc xây dựng FAirServer cho hệ thống FAirNet giám sát nồng độ bụi (PM)
5  Phan Anh Xây dựng VNU Virtual Campus bằng công nghệ 4D GIS
6  Hoàng Xuân Phương Phân tích, thiết kế và phát triển hệ thống giám sát chất lượng không khí (APOM) theo chuẩn OGC (Open Geospatial Consortium)
7  Trần Mạnh Tiến Xây dựng hệ thống quản lý sân bóng đá trên nền tảng Android (phần server)
8  Nguyễn Văn Dương Xây dựng hệ thống quản lý sân bóng đá trên nền tảng web.
9  Nguyễn Văn Điệp Xây dựng hệ thống quản lý sân bóng đá trên nền tảng Android (phần client)
10  Nguyễn Văn Hưng Xây dựng Oauth 2.0 server cho các ứng dụng giám sát tài nguyên môi trường
11  Vũ Hồng Phú Nghiên cứu xây dựng nền tảng điện toán đám mây phục vụ phát triển Hạ tầng thông tin không gian
12  Nguyễn Quang Huy Nghiên cứu và phát triển phương pháp phát hiện giàn khoan sử dụng ảnh Sentinel – 1A
13  Lưu Quang Thắng Phát triển công cụ tự động xây dựng bản đồ chuyên đề cho các hệ thống WebGIS

Một số hình ảnh trong lễ bảo vệ:

Bạn Hà Đức Văn, sinh viên lớp K58D

Bạn Hà Đức Văn, sinh viên lớp K58D

Bạn Phạm Văn Chính, sinh viên lớp K58CLC

Bạn Phạm Văn Chính, sinh viên lớp K58CLC

Bạn Phạm Mạnh Cường, sinh viên lớp K58CC

Bạn Phạm Mạnh Cường, sinh viên lớp K58CC

Bạn Ngô Khắc Thành, sinh viên lớp K58T

Bạn Ngô Khắc Thành, sinh viên lớp K58T

Bạn Phan Anh, sinh viên lớp K58CLC

Bạn Phan Anh, sinh viên lớp K58CLC

Bạn Hoàng Xuân Phương, sinh viên lớp K58T

Bạn Hoàng Xuân Phương, sinh viên lớp K58T

Bạn Trần Mạnh Tiến, sinh viên lớp K58CLC

Bạn Trần Mạnh Tiến, sinh viên lớp K58CLC

Bạn Nguyễn Văn Dương, sinh viên lớp K58T

Bạn Nguyễn Văn Dương, sinh viên lớp K58T

Bạn Nguyễn Văn Điệp, sinh viên lớp K58T

Bạn Nguyễn Văn Điệp, sinh viên lớp K58T

Bạn Nguyễn Văn Hưng, sinh viên lớp K58T

Bạn Nguyễn Văn Hưng, sinh viên lớp K58T

Bạn Vũ Hồng Phú, sinh viên lớp K58T

Bạn Vũ Hồng Phú, sinh viên lớp K58T

Bạn Nguyễn Quang Huy, sinh viên lớp K58T

Bạn Nguyễn Quang Huy, sinh viên lớp K58T

Bạn Lưu Quang Thắng, sinh viên lớp K58T

Bạn Lưu Quang Thắng, sinh viên lớp K58T

Thầy và trò FIMO sau buổi bảo vệ thành công.

Thầy và trò FIMO sau buổi bảo vệ thành công.

Một lần nữa xin chúc mừng các bạn sinh viên, chúc các bạn thành công trong cuộc sống.