Artificial Intelligence (AI) has become a word that symbolizes the next stage of innovative technological transformations and how to direct the future industry. With the use of intelligent algorithms, data classification and intelligent prediction analysis, artificial intelligence has been effective in many sectors. The term combining the sensitive aspect of GIS with artificial intelligence methods with effective analysis and solution-based approach of artificial intelligence is expressed as Geospatial AI. Spatial Artificial Intelligence may also be called a new form of machine learning based on a geographic component.
How does it work?
With the help of simple smartphone applications, people can give real-time feedback on the conditions around them, such as traffic congestion, peak hours, user experiences, ratings (low, medium or busy, etc.). The data is then collated, sorted, analyzed and thus increased accuracy and precision due to the large number of users contributing to the database. This approach to the use of location information will not only interpret the missing information, but will also help more efficient solutions for specific geographical areas. For example, it can predict which route in the city will face the most congestion, or the roads to which the traffic load should be distributed or the traffic flow to be redirected.
Various applications of Spatial Artificial Intelligence
As an example, traffic congestion is a problem that we deal with almost every day in returning from our homes to workplaces and back home. However, Spatial Artificial Intelligence (Geo.AI) applications are available in a variety of industries including location and GIS users. Travel sharing companies, logistics, agriculture, measurement and infrastructure are some of the outstanding examples. Uber, Lyft etc. travel sharing companies may use feedback from customers to determine the density of cars and whether they are suitable for customer acquisition. In the logistics and supply chain, Spatial Artificial Intelligence can obtain more accurate location information that can facilitate product delivery and save time. Today, in the cloud, there are project-based systems, each with a large amount of data storage and all based on the deep learning principle that works to solve the same problem. However, it is a fact that the use of deep learning at the automation level for the past few years has not been deemed appropriate either because of cost constraints or limitations in technology implementation. Similarly, Spatial Artificial Intelligence capabilities will be developed to serve multiple purposes as it accommodates the location component in artificial intelligence applications for industry purposes. In general, Spatial Artificial Intelligence in the business world will significantly improve planning, resource allocation and decision-making, predict the increase in demand and supply, identify high and low margin expectations, improve supply chain efficiency and optimize service delivery in various sectors. Symptoms of Spatial Artificial Intelligence wherever positioning information is analyzed; it will continue to appear more often with the ease of our work and the ability to decide for us!