Major advances in AI(Artificial Intelligence) has been made to unlock the potential of GIS. The GIS community has been using AI for a very long time. As all GIS systems contain a wealth of information classified by geographical locations, these make excellent training data sets for AI systems. Till now, there have been some amazing successful attempts to use GIS along with AI, be it for pollution management or disaster management. We can safely say that AI is at a point where it is already making a massive impact everywhere and this includes the geo spatial realm as well.
Basically, the geo spatial realm consists of two types of data – Structured and Unstructured. The structured data is the vector data, which consists of parcel boundaries, roads, GPS breadcrumbs and so on. Another is unstructured data which is raster data, which typically refers to voice and text. While being extremely useful for humans, it is difficult for machines to extract actionable information. But, this is changing rapidly. Deep learning – so-called because it applies deep artificial neural networks that give hundreds of connected layers of calculation, has enabled a new revolution in the processing of unstructured data. For those of us who work in the geo spatial field, this means a massive increase in productivity.
Where Is This Whole Thing Going, And What Are The Problems Faced?
1. Infrastructure Challenges
Maintaining a GIS system requires complex hardware and software components since it processes a lot of visual data and requires significant storage resources. It can be an obstacle for smaller companies for cost and maintenance. Most projects attempting GIS require a reasonable budget and that makes it difficult.
2. Data Structure Challenges
To work efficiently with AI, GIS systems require a large number of data points to learn from. The diversity of data is another problem, as very similar data can lead to over fitting the model, with no new insights. Additionally, GIS relies on significant data generalizations and information simplification.
3. Human Error
This can happen all the time. The developer must have in-depth knowledge about data science, machine learning, and geography. Now, all of these are different things and require time to master. They need to understand the industry and collaborate with specialists from other areas. Decision making also plays an important role in this. People need to focus on the ability of GIS to produce maps that are easy to understand and use, compared to raw data on the table.
To overcome these challenges, Geo AI – the combination of GIS and AI is beginning to take control. Applying AI within a spatial context is an interesting concept and requires massive support from the AI and machine learning industry. Mobile app developers who are putting their best on the table are also necessary for betterment and improvement.