Interactive Workshop on Artificial Intelligence & Deep Learning By invitation only


24 Jan 2017 0900 - 1100 hrs


  • Budhendra Bhaduri, Director, Urban Dynamics Institute, Oak Ridge National Laboratory, USA
  • Joseph Rajoy Kuttikat, Senior Principal Product Manager, Oracle, India
  • Anand Kannan, Fellow Tech Ladder, Pitney Bowes, Canada
  • Aswani Kumar Akella, Founder, Latgeo Consulting, India
  • Prof. Arup Dasgupta, Managing Editor, Geospatial World, India
  • Col Inderjeet Singh, Director – Smart Cities, Scanpoint Geomatics, India
  • Dr. Kumar Navulur, President, DigitalGlobe Foundation, USA

Artificial Intelligence & Deep Learning Transforming Industries: What's in it for Geospatial?

Geospatial World Forum 2017 carries the theme 'Geospatial + Deep Learning: Shaping Smarter World', in an attempt to take a glimpse into the future of geospatial industry, where artificial intelligence and deep learning will be driving geospatial data exploration, analytics and visualisation helping us derive meaningful insights for a smarter world. More details on conference theme is available here.

When talking about deep learning, the general understanding would be machines being able to recreate the processes that the human brain performs. By using certain algorithms, machines are trained to recognise, identify and understand patterns in the data. As an example from geospatial data point of view, machines equipped with deep learning can rapidly process gigabytes of satellite imageries and extract meaningful information through automated pattern recognition and object detection process, saving hours of critical response time.

So how will this change business and society?

Artificial intelligence and deep learning have been applied to a vast range of industries, from healthcare, to finance, advertising and retail, to manufacturing and transport. Virtual assistants for personal healthcare use deep learning to recognise symptoms, checking on medication adherence and mental health that allows healthcare providers to understand patients' needs, and therefore, organise more efficient use of doctors' time.

Financial services use deep learning tools and techniques to make better stock market predictions, move data more securely, and detect fraudulent activity quicker to ensure smart and timely actions can be taken. The trained machines can also analyse and understand how customers are spending, investing and making financial decisions, in order to deliver a more personalised service to account holders.

Another example is in the rail industry. Collecting and analysing real-time data throughout train operations enable comprehensive surveillance capability to rail companies, allowing what is called 'predictive maintenance'. Deep learning allows massive-scale analysis for anomalous patterns, generating critical clues for impending failures, which gives enough time for maintenance team to take action without causing any operational delay.

As artificial intelligence and deep learning continue to transform industries, we wonder what's in it for geospatial community. How can we take advantage in the increasing interest surrounding location technology and educate industries that location data is the key enabler in producing insights for business intelligence through deep learning. The workshop shall be a platform for geospatial players to meet and share ideas with experts from various industries on geospatial opportunities in the uprise of artificial intelligence and deep learning.

Workshop Format

  • The workshop will be discussion-oriented (without presentations). This will ensure optimum value of the time by discussing only on relevant questions.
  • The workshop will start with the discussion on a draft document, which is shared in advance with the participants
  • The workshop shall be followed by two other workshops discussing sub-themes of Artificial Intelligence & Deep Learning: Autonomous Vehicles and Internet of Things .

Target Panelists

  • AI and robotic experts
  • Geospatial experts
  • Head of Analytics from various industries: healthcare, finance, transport, retails, advertising, etc
  • Location data providers
  • Researchers