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CyberGIS Center, University of Illinois at Urbana-Chmapaign

Applications of Machine Learning in Geospatial Studies

Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn and improve from data without being explicitly programmed. In the geospatial field, machine learning has been applied to many applications, including predicting earthquakes, analyzing satellite imagery, and detecting fake news.

One example of machine learning in the geospatial field is using machine learning algorithms to predict earthquakes. Researchers at the University of California, Los Angeles, used machine learning to analyze data from over 600,000 earthquakes and found that specific patterns in the data could be used to accurately predict future earthquakes (Cuffley, 2022). This has the potential to save lives and prevent damage by providing early warning systems for areas at risk of earthquakes.

Another application of machine learning in the geospatial field is the analysis of satellite imagery. Machine learning algorithms can automatically identify and classify objects in satellite images, such as roads, buildings, and bodies of water (Zaabar et al., 2021). This can be useful for various applications, such as mapping urban growth and monitoring environmental changes.

Additionally, machine learning has been applied to detecting fake news in the geospatial field. Researchers at the University of Maryland used machine learning algorithms to analyze the spatial and temporal patterns of fake news. They found that fake news is more likely to spread in urban areas and during political and social instability (Qiu et al., 2019). This can help identify and combat the spread of fake news and improve the accuracy and reliability of geospatial information.

Overall, the use of machine learning in the geospatial field has the potential to improve our understanding of the world around us and provide valuable insights for a wide range of applications.


  • Cuffley, A. (2022). Social Media Misinformation and the Prevention of Political Instability and Mass Atrocities • Stimson Center. Stimson Center. Retrieved from
  • Ma, S. L., Liu, Y. H., & Jiang, S. F. (2022). Structural performance assessment by acceleration measurements based on null space method and fast, independent component analysis for the frame-shear wall structure under earthquake wave. Journal of Civil Structural Health Monitoring, 12(5), 1027-1041.
  • Zaabar, N., Niculescu, S. and Mihoubi, M., 2021. Assessment of combining convolutional neural networks and object-based image analysis to land cover classification using Sentinel 2 satellite imagery (Tenes region, Algeria). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, pp.383-389.




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