How can you differentiate between supervised and unsupervised GIS machine learning?

2 Unsupervised machine learning

Unsupervised machine learning is a technique that uses unlabeled data to discover hidden patterns, structures, or clusters within the data. Unlabeled data means that there is no predefined outcome or category for each data point, and the model has to find its own way of organizing the data based on its similarities or differences. Some examples of unsupervised machine learning algorithms are k-means clustering, hierarchical clustering, principal component analysis, and self-organizing maps. Unsupervised machine learning is useful for GIS applications that require exploratory analysis, data reduction, or anomaly detection, such as spatial clustering, dimensionality reduction, or outlier identification.

4 Examples of supervised and unsupervised GIS machine learning applications

To illustrate the difference between supervised and unsupervised machine learning, here are some examples of GIS applications that use either technique or both. For instance, land cover mapping can be done using supervised machine learning, where the model is trained with labeled images that have predefined land cover classes and then applied to new images to produce a land cover map. Alternatively, it can be done with unsupervised machine learning, where the model is applied to unlabeled images and clusters them into different groups based on their spectral or spatial features. Spatial clustering is another GIS application that involves grouping spatial data into clusters based on their proximity or similarity. This can also be done using supervised or unsupervised machine learning. Lastly, spatial interpolation is a GIS application that involves estimating the values of a variable at unknown locations based on the values at known locations. This can be done with supervised machine learning where the model is trained with labeled data that have the variable values at some locations and then applied to new data to predict the variable values at unknown locations. Alternatively, it can be done using unsupervised machine learning, where the model estimates the variable values at unknown locations based on interpolation or extrapolation methods.

5 Benefits and challenges of GIS machine learning

Machine learning can offer many advantages for GIS applications, such as enhancing accuracy, efficiency, and scalability of spatial analysis and modeling, automating the processing and interpretation of large and complex spatial data, generating new insights and knowledge from spatial data, and supporting decision making and problem solving in various domains and contexts. However, it also poses some challenges for GIS applications, such as selecting the appropriate algorithm and parameters for the data and problem, evaluating the performance and reliability of the model and results, explaining the logic and rationale of the model and results, as well as addressing the ethical and social implications. It is crucial to understand the strengths and weaknesses of machine learning to apply it with caution and critical thinking.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?