Most of the big Fortune 500 companies today are problem-solvers, and it won’t be an overstatement to say that over 90% of them have a spatial use-case. Salience of geospatial is a recurring thread all across. This is because everything happens in a space and time continuum.

With a lot of startups trying to study weather patterns, track pollution, or map carbon footprint, geospatial takes the center-stage in Climate Change mitigation efforts as well.

Similarly, ensuring food security is of utmost importance for the future of humanity. With the help of geospatial-based smart technology, we can decrease operational costs and simultaneously boost production to feed the growing population.

Versatile Applications

De facto, the geospatial industry is highly map-centered. Though increasingly, AI and predictive analytics are of great utility for everything from weather forecasting to agricultural production.

Spatial use-cases are ubiquitous across all industries, though the degree and magnitude varies. For instance, over a decade back the insurance sector was not geospatially context-driven, while today most insurance companies have a spatial strategy.

Whether it is claim forecasting for property damage due to disasters, or underwriting process, the spatial component is absolutely critical. Organizations are purchasing spatial technology and embedding it into their stacks.

There is really no alternative to spatial data for providing insights on the most pressing challenges to planners and decision-makers.

“AI and predictive analytics are of great utility for everything from weather forecasting to agricultural production.”

AI Outlook

Cloud has emerged as a true game-changer in the geospatial domain with the proliferation of data and the rise of near realtime actionable insights across use-cases and verticals, and applications with many uses, enabling near real-time actionable insights.

What’s most significant for the geospatial industry is the spatial attribute and the ability to get maximum functionality and tech stack optimization for spatial data infrastructure, analytics, and moving forward, an organization’s AI needs.

There are multiple dimensions defining data, but location and time are the most crucial ones. However, they are incomplete without a spatial context. A common challenge that users face is integrating data sources and creating a unified spatial context to derive value.

An underlying trend these days is bringing spatial context into data stack instead of having a completely new GIS stack. One of the limitations is that be it conventional Cloud data infrastructure companies such as AWS, Azure, or Google, or even specialized data warehousing solutions, they don’t treat spatial analytics and AI as first class citizens. This is where Wherbots Cloud comes in.

We are building a Cloud data infrastructure for spatial analytics and AI, which would redefine a lot of things. Solving the data integration riddle is at the core of what we do.

Disclaimer: Views Expressed are Author's Own. Geospatial World May or May Not Endorse it