The adoption of AI in Geographic Information System (GIS)

By: Vajratiya Vajrobol, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan,

The combination of Geographic Information Systems (GIS) with Artificial Intelligence (AI) has substantial possibilities for improving spatial analysis, decision-making procedures, and automation across diverse sectors. This is an overview of the benefits of the synergy between GIS and AI.

1. Analysis of Spatial Data

A framework for organising and evaluating spatial data, such as maps, satellite images, and geospatial databases, is provided by GIS. With this spatial data, AI algorithms may be used to uncover patterns, spot trends, and acquire insights that might be hard to find using more conventional analysis techniques [1].

2. Modelling Predictively

GIS may employ AI, especially machine learning, to build predictive models. Predicting patterns of urban growth, evaluating flood risks, or pinpointing possible sites for new infrastructure projects are a few examples. Making decisions based on facts and with more accuracy is made possible by this combination [2].

3. Remote Sensing and Image Recognition

Drone or satellite imagery can be analysed using AI techniques like computer vision. This provides useful information for urban planning, environmental monitoring, and agriculture by automating the detection and classification of elements like land use, vegetation, or infrastructure [3].

4. Analysing and Monitoring in Real-Time

Real-time tracking and analysis of dynamic spatial data is made possible by the combination of GIS and AI. For example, keeping tabs on disease outbreaks, traffic patterns, or the effects of natural disasters [4].

5. Smart Cities and Urban Planning

GIS and AI help to create smart cities. City planners may create more sustainable and effective urban environments by using AI algorithms to assess environmental elements, traffic patterns, and demographic data [5].

It is anticipated that when GIS and AI work together, new applications, better decision-making techniques, and a greater comprehension of the spatial components of different phenomena will result.


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  3. Kentsch, S., Lopez Caceres, M. L., Serrano, D., Roure, F., & Diez, Y. (2020). Computer vision and deep learning techniques for the analysis of drone-acquired forest images, a transfer learning study. Remote Sensing, 12(8), 1287.
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  8. Stergiou, C. L., Psannis, K. E., & Gupta, B. B. (2021). InFeMo: flexible big data management through a federated cloud system. ACM Transactions on Internet Technology (TOIT), 22(2), 1-22.
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Cite As:

Vajrobol V. (2024) The adoption of AI in Geographic Information System (GIS), Insights2Techinfo, pp.1

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