Neural AI fusion with GIS, door to new possibilities?

GIS or Geographic Information System is complicated. It requires a thorough reasoning mechanism to deal with the raster and spatial data information (Click here for more on spatial and non spatial data). But in some cases when dynamic data is used in GIS systems, the decision making may not be correct using only GIS systems. It needs some intelligent reasoning to handle dynamic data. Here comes Neural AI (Artificial Intelligence) into the picture which when used with GIS systems in combination  can provide an appropriate modeling and decision making system for dynamic spatial data.

What is Neural Artificial System?

The Neural Artificial System or the Artificial Neural Network (ANN) is an information processing system that is inspired by biological data processing system of Human Brain. This system is composed of many nodes just like human brain is composed of neutrons. This system processes sensitive and dynamic data with sophisticated reasoning and use heuristic algorithms.

How GIS and Neural AI Work Together?

The advanced techniques like artificial intelligence are proven to be very efficient to promote the data and information processing in GIS. Neural Artificial networks can easily model the real world situations which is required for GIS to model dynamic spatial data. The models are used to measure and quantify the factors of spatial phenomena and spatial data. Following are the usage of AI and GIS together –

  • AI helps GIS systems to select more appropriate spatial patterns when creating spatial model using dynamic spatial information.
  • AI extracts data from multifunctional complex raw information and GIS uses this neural computing to predict the pattern of spatial information about any real phenomena.

Implementation of Neural Computing in GIS:

  • Remote Sensing – Complex GIS systems use remotely sensed images sent by satellite. With the help of Neural computing, it becomes easy to analyse various imageries and determine the spatial patterns. For example, landscape patterns the shape, color, connectivity etc. can be determined through analysis of pixels using neural computing.
  • Transportation: The traffic situation vary from city to city, time to time and day to day. This is a very dynamic information to be analyzed. The GIS system can identify the road conditions or road network but predicting an unknown behavior like accident or traffic congestion etc. needs a sophisticated processing of dynamic data from the factors involved. AI fused with GIS can help predict weather conditions, flow of traffic, information about time etc. to. It can surely provide vital contribution in development of smart cities.
  • Marketing and Business: The current market situation, the location of demand and supply which changes dynamically throughout the day – these kind of data is well handled by AI in GIS. Using the Neural Network analysis the time, traffic flow, market situation demand and consumer etc can be easily predicted.

There is a lot to improve in both AI and GIS systems but it can be said that AI is the future of dynamic and intelligent spatial systems which will be used widely. Neural Artificial Intelligence will be used for predictive analysis which is very important to make decisions about real geospatial phenomena.

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