Geospatial and Deep Learning to transform various industries

Geospatial and Deep Learning to transform various industries


In this blog post, we explore how Geospatial and Deep Learning will transform various industries such as Agriculture, Smart Cities, Transport Infrastructure, Defence and more!


  • Machine learning techniques deployed to study crop patterns using the global EO satellites archives
  • Predictions on global crop patterns are turning out to be more accurate, more frequent, and more granular
  • Measuring Sustainable Development Goal promoting sustainable farming will bring in new analytic tools
  • Industries such as insurance, finance, and commodity trading to depend on these more accurate predictions

2Defence and National Security

  • Live and predictive maintenance using satellites, UAVs and other sensors to be the future
  • Situational awareness for global urban warfare needs live insights from real-time data streams and analytics, fueled by deep learning platform
  • Such platform provides advanced autonomous functions, facilitating the dissemination of critical insight and intelligence across chain of command
  • Predictive analytics will help mitigating risks and allowing thorough preparedness for possible eventualities

3Smart Cities

  • Connected sensors to be deployed across cities
  • IoT network to transmit data to city governments
  • Data stored in the cloud for easy accessibility and sharing
  • Automation in processing of data deriving patterns
  • intelligence through Analytics and dashboards

4Transport Infrastructure

  • Connected cars and autonomous vehicles to drive the IoT adoption
  • Integration of data from other sensors such as weather, traffic etc. becomes evident for better predictions using big data and AI
  • Railway infrastructure would also be driven by IoT and connected devices
  • Collecting and analysing real-time data for train operations enables ‘predictive maintenance’

5Energy Resources

  • Status of global oil reserves and their transportation can be monitored in real time with systems bacekd by AI
  • Automotic recognition of global energy generation through renewables such as solar, wind can be mesaured using geospatial data driven by deep learning methods
  • Analysis of complex seismic and environmental data to identify optimal sites for energy exploration is more accurate and time-effective via deep learning

6Land Administration

  • Land administration systems, still face problems due to lack of data
  • Data consolidation using structured forms prone to delays and challenges
  • Way to exploit utilization of unstructured cadastral records would bring possible solutions
  • Big data can approach to manage unstructured data can bring in possible solutions
  • Integration with AI for understanding land change patterns would be the key


  • New types of global epidemics on the rise such as Zika, Dengue, Chikungunya
  • Understanding these requires deep understanding of the origin, pattern of spread and related trends
  • Monitoring environment, urban landscapes and predicting such patterns needs to be explored using AI
  • Wearables and Virtual assistants driven by cloud and predictive analytics will also drive the health industry where location technology will play a leading role

8Water Resources


  • Monitoring water quality through in-situ sensors is expensive and is not comprehensive, where EO takes the lead
  • Studying historic patterns of global water bodies and predicting future trends will be possible through AI
  • Global monitoring systems for achieving 2030 agenda for Sustainable development will be the key


Suggested Reading
Infographic – How Deep Learning is Shaping a Smarter World
AI and Deep Learning and geospatial industry – The transformation

Understand these insights from global experts

Join us as at Geospatial World Forum 2017 as we unlock the connection between geospatial ecosystems and deep learning, and understand how the integration of both can produce ultimate knowledge for shaping a smarter World.