Making GeoSpatial/Location processing feasible using AI

Artificial Intelligence (AI) has become a buzzword that symbolizes the next stage of innovative technological transformations. All the industry in the future would be driven by this. This arising scientific discipline, called geospatial artificial intelligence (GeoAI), which “combines innovations in spatial science, artificial intelligence methods in machine learning and deep learning, data mining, and high-performance computing to extract knowledge from spatial big data” will in particular improve existing and create new technologies for geospatial information systems (GIS).

Geospatial AI in all the domains

Knowing traffic congestion through mobile with Geospatial AI is the most promising application everyone uses as we grapple with the traffic almost every day. But the applications of Geo.AI are in a number of sectors, including those that use location and GIS. Ride-sharing companies, logistics, farming, surveying, health, agriculture and infrastructure are some of the prominent examples.

Ride-sharing companies like Uber, Lyft etc. can take similar feedbacks from customers and process the data to find out the density of cars and the availability of drivers.

In logistics and supply chain, Geo.AI can plug the gaps and gather more accurate location information that can streamline product delivery and save time.

This tool is working great for taking images from the space. There are two primary components for this application – The inputs are from geospatial mapping or remote sensing. AI has been used in conjunction with geospatial analysis. Hence satellite imagery portrays the ground reality without actually requiring physical presence. It is accurate at high-resolution, available at the needed level of frequency, and has become relatively inexpensive in recent times.

Geospatial AI serves with great opportunities and applications in healthcare, as location plays a key role in both population and individual health. Several disciplines within the domains of public health, precision medicine, and IoT (Internet of Things)-powered ‘smart healthy cities and regions’ are benefiting from GeoAI, e.g., environmental health, epidemiology, genetics and epigenetics, social and behavioral sciences, and infectious diseases, to name but a few.

Moreover, geo-tagged big data collated from rich sources, such as social media streams, satellite imagery (remote sensing), IoT sensors in smart cities (e.g., monitoring air, light, and sound pollution), and personal sensing (via connected ambient and wearable sensors), can be reasoned with using geospatial AI to answer many important research and practice questions in more comprehensive ways.

Besides these population-level geospatial AI applications, there are further opportunities for integration of GeoAI and location-based information intelligence into precision medicine via well-tailored Health (mobile health) interventions targeting individual patients.

It is now commonplace for a project based on deep learning to simultaneously operate multiple machines in the cloud, each with a large amount of data storage and memory and all working to tackle the same problem. However, it was not considered feasible few years ago.

Similarly, Geo.AI capabilities would be enhanced as it is more widely embraced by the industry, and incorporating the geographical and location component in AI would serve multiple purposes.

Overall, in the realm of business, Geospatial AI would substantially improvise planning, resource allocation, and decision-making – predicting the surge in demand and supply, identifying the prospects of high and low margin, multiplying supply chain efficiency, and optimizing service delivery. The scope of Geospatial AI is simply endless.

Sometimes called geospatial AI, geospatial analytics, or GEOINT (geospatial intelligence), the intersection of geospatial data and artificial intelligence will be critical to enterprises and governments ranging from weather centers, national labs, defense agencies, healthcare, agriculture, insurance, transportation, and many more.

Behind the rise of geospatial AI are three trends: increased availability of geospatial data from satellites and remote sensing, the advancement of artificial intelligence (particularly machine and deep learning), and the availability of massive computational power.

Here Comes Supercomputing

Widespread adoption of geospatial AI can’t happen without supercomputing. The digital universe is doubling in size every two years, headed for 175 zettabytes by 2025, and AI applications thrive on massive datasets. Add to this the growing interest in and need for distributed machine and deep learning algorithms in less time by parallelizing training computation across multiple machines.

Supercomputers are tightly integrated, highly scalable, zero-waste architectures that offer the right technology for each task to enable maximum application efficiency and eliminate computational bottlenecks.

Yes, it’s magical

For the business leaders, the Geospatial Artificial Intelligence revolution offers a tantalizing promise: almost unlimited opportunity when coupled with imagination. It’s magnificent that all business sectors or government entities can use these technologies to upscale their programs. Now it depends on the leaders how smartly they use it.