Table of Contents
Artificial Intelligence made a major footstep that has been arising independently in GIS. During the past ten years, there is significant convergence in GIS and AI. GIS is an admired technology that provides vast data sets and wide scope of applications for AI. Recent Projects were designed to examine and compare different techniques for predictive modeling of Geographic Phenomena distributions.
Main Goals Of AI In GIS
It aims to utilize techniques in artificial intelligence to promote intelligent information processing in GIS. Moreover, it gives excellent solutions for modeling, such as real-world applications.
AI methods in GIS applications have mainly following goals are:
- Improve selective approaches in spatial patterns.
- Access the predictive accuracy and appropriates of spatial modeling technique independently and as an integrated model for various datasets.
- AI techniques to gain insight into critical spatial functions and processes through rule extraction and factor sensitivity tests.
Applications Of AI In GIS
- Optical character recognition.
- Handwriting recognition.
- Speech recognition.
- Face recognition.
- Artificial creativity.
- Computer vision.
- Virtual reality.
- Image processing.
One of the main applications in GIS is Data Mining. Data Mining is a step in the knowledge discovery process that automatically detects patterns in data. Geographic Data Mining(GDM) is a type of data mining that seeks to apply standard data mining tools modified to consider the unique features of geographic data and objectives and needs of GIS.
Objectives Of GDM
Data mining tools can be used to deal with geospatial data, and they should be adopted to do so. However, the characteristics of data types and specific GIS-minded analysis can require several adjustments in the data mining methods. These adjustments are dependent on the expected results and analysis.
One of the popular methods in the Data Mining field is the Self-Organizing Map(SOM). SOM is a technique used mainly in visualization, data abstraction, and clustering tasks. It allows the users to investigate the data structure, and it will enable an improved understanding of the data. The most helpful feature of SOM is density estimation capability. SOM is also used in cartographic representation, spatial data mining, and in several cases of the location/allocation problem.
Data Mining Imagery
Methodology Of Data Mining
Data Mining having Six standard techniques such as
1. Anomaly Detection
It defines the outlier or change or deviation detection.
2. Association rule learning
It is also known as dependency modeling. The relationship between variables for searches and used as market-based analysis.
3. Clustering
It is used in the form of defining the ‘similar’ without using the known structure of the data in the task of discovering groups and structures.
4.Classification
It is the task of generalizing known structures to apply to new data.
5.Regression
It is used to find a function that models the data with a minor error for estimating the relationships among data or datasets.
6.Summarization
It is a more compact representation of the dataset, including visualization and report generation.
Conclusion
Using AI GIS to improve and develop the new generation of GIS technology systems is an effective method to solve the current intelligent problem of GIS systems. AI GIS has initially realized the computer vision extraction of geographic information such as remote sensing images, videos. And one can introduce AI technologies such as voice recognition and language processing technologies and realize further empowerment. However, AI GIS is still in the Narrow AI stage and is far from the general AI (AGI) stage. Therefore, the technology represented by general AI is also an essential direction for the future development of AI GIS.
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