For past few years, there has enough talk about how technologies like Artificial Intelligence along with Big Data, Cloud and Internet of Things (IoT) will change the way how we see the world. No doubt, there has been a continuous growth in digital infrastructure and digital technology ecosystem with these evolving technologies. This emergence is further accelerating the geospatial industry’s global reach and contribution. In time to come, we are going to see a convergence of geospatial technologies with AI and its subsets namely machine learning (ML) and deep learning (DL). This way, we are witnessing scenarios where AI is aiding in the transformation of geospatial technologies and on the other hand geospatial technologies are also helping in transformation of AI, and other future technologies.
Though this transformation is yet to be fully matured, it becomes important for the industry stakeholders to start discussing on this impact and how we aim to stand in the future. While the use of geospatial in the traditional sectors continues to rise, the future of geospatial market lies in automation and trending technologies. Let’s have a look at how these technology will alter the Geospatial sector and vice versa.
Artificial Intelligence (AI) has captured the fascinated attention of all industries. From decision-making to computing to robotics to vehicles and even cosmetics, AI has left its mark everywhere and it will usher in the grandest social engineering experiment in the history of the world.
AI encompasses many technologies like cognitive computing, machine learning, language processing, neural networks, data analysis, information retrieval, genetic and evolutionary computation, knowledge discovery, machine vision, and, of course, the latest catch word, deep learning. All these techniques have one common thread: how to mimic human faculties like vision, thought processes and reasoning using computers.
As all GIS systems contain a wealth of information classified by geographical locations, these make excellent training datasets for AI systems. This connection to AI is natural in the context of recent advances in computer vision and image recognition. By now, there have been some successful attempts to use GIS and AI for pollution management or to control disease spreading.
As location intelligence becomes more and more relevant across industries, Big Data and its analytics is what the future holds. Big Data characterized by five Vs — Volume, Velocity, Variety, Veracity and Value. While Volume is easily understood, Velocity, Variety, Veracity and Value lie in our ability to take fast-moving data and convert it into something meaningful through analytics.
Traditional geospatial data, which includes remotely sensed data, is structured and stored for analysis post facto in analytical systems like GIS. However, modern data with useful geospatial content like photos, social media chats, video, voice and messages now constitutes almost 80% of the total data. In its unstructured form, it cannot be used in conventional analytic systems like GIS because the sheer volume far exceeds the data storage capacity available. It also has a high velocity, but its veracity may require curation. Big Data improves innovation, sustainability and translates into billions in savings.
Geospatial systems are solutions in search of innovative technologies. The emergence of Cloud computing provides a platform for the evolution in Big Data Analytics for geospatial data with an elastic, on-demand computing platform to integrate — observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback — the essential elements of the geospatial sciences.
In recent years, EO data have become available from governmental agencies as a result of the ever-increasing technological capabilities of the Web. We have also witnessed IT companies like Microsoft, Amazon and Google investing heavily in Web keeping distribution and analysis of geospatial data in mind.
Internet of Things
Fundamentally, IoT is about devices connected to the Internet or Machine-to-Machine (M2M) communication. But, the real value of this technology is not based in the tons of data these sensors and devices are gathering every second of the minute. Its true potential depends on that data’s analysis, and on how Cloud-based applications leverage that information. Currently, the Internet is human-orientated. The change toward machine learning or the IoT will need to take into account devices which are, for all intents and purposes, autonomous and act independently whether or not any person or any system is actively using them. UNGGIM predicts that in the next 5 to 10 years, we may see significant developments in the architecture of the Internet.
Needless to say, this kind of next-generation market opportunity is pulling in tens of millions in venture funding for startups as well. In 2014 alone, over $1.6 billion was invested into IoT companies by venture capitalists. 2020 will be a big year to watch out for while talking about the Internet of Things. As many as 50 billion devices will be connected to the Internet by 2020, while there is expected to be a rise of over 285% in connected devices by that time.
To learn more visit Geospatial World Forum 2020