{"id":17580,"date":"2018-05-15T20:43:17","date_gmt":"2018-05-16T01:43:17","guid":{"rendered":"http:\/\/gisgeography.com\/?p=17580"},"modified":"2025-04-01T05:34:00","modified_gmt":"2025-04-01T10:34:00","slug":"deep-machine-learning-ml-artificial-intelligence-ai-gis","status":"publish","type":"post","link":"https:\/\/gisgeography.com\/deep-machine-learning-ml-artificial-intelligence-ai-gis\/","title":{"rendered":"The Rise of Machine Learning and AI in GIS"},"content":{"rendered":"<style>.kb-image17580_13d7e7-a0 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<figure class=\"wp-block-kadence-image kb-image17580_13d7e7-a0 size-medium_large\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"352\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-768x352.jpg\" alt=\"Machine Learning Artificial Intelligence GIS\" class=\"kb-img wp-image-61386\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-768x352.jpg 768w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-300x138.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-678x311.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-50x23.jpg 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-200x92.jpg 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-425x195.jpg 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-550x252.jpg 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-115x53.jpg 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS-360x165.jpg 360w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/05\/Machine-Learning-Artificial-Intelligence-GIS.jpg 850w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Machine Learning and Artificial Intelligence in GIS<\/h3>\n\n\n\n<p>Machine learning in GIS is like giving the world&#8217;s most <strong>powerful magnifying glass<\/strong> to a cartographer. It enables us to uncover hidden patterns in geographic data, transforming landscapes into libraries of spatial intelligence.<\/p>\n\n\n\n<p>In simple terms, machine learning <strong>makes sense out of noisy data<\/strong> finding patterns that you&#8217;d never thought existed.  Some say it&#8217;s like software that writes software. <\/p>\n\n\n\n<p>Instead of applying a pre-built function, ML gains experience through repeated seen conditions and builds a model to apply in new situations.<\/p>\n\n\n\n<p>AI is here to stay.  But how can we use it in the context of GIS? Let&#8217;s explore this question today.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Types of Machine Learning (ML)<\/h3>\n\n\n\n<p>The two broad categories of machine learning are <strong>supervised<\/strong> and <strong>unsupervised<\/strong>.  And they both can apply to GIS applications in various ways.  First, what\u2019s the difference between the two?<\/p>\n\n\n\n<p><strong>SUPERVISED LEARNING<\/strong> is just fitting data to a function for prediction.  For example, if you plot millions of sample points in a graph, you can fit a line to approximate a function. <\/p>\n\n\n\n<p><strong>UNSUPERVISED LEARNING<\/strong> recognizes what the data is using patterns from unlabelled data.  For example, it takes millions of images and runs them through a training algorithm. After trillions of linear algebra operations, it can take a new picture and segment it into clusters.<\/p>\n\n\n\n<p>Most importantly, machine learning is about optimally solving a problem.  So it automatically <strong>learns on its own<\/strong> and <strong>improves from experience<\/strong>.<\/p>\n\n\n\n<p>Lately, GIS is applying artificial intelligence in areas such as classification, prediction, and segmentation.  Two of the most popular frameworks are <a href=\"https:\/\/www.tensorflow.org\/\">TensorFlow<\/a> and <a href=\"https:\/\/pytorch.org\/\">PyTorch<\/a>.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">1. Image Classification (Support Vector Machine)<\/h3>\n\n\n\n<p>When you look at a satellite image, it&#8217;s not always easy to know if you are looking at trees or grass\u2026 or roads vs buildings. So imagine how hard it would be for a computer to know.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"267\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/svm-support-vector-machine-300x267.png\" alt=\"svm support vector machine\" class=\"wp-image-17585\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/svm-support-vector-machine-300x267.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/svm-support-vector-machine-50x44.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/svm-support-vector-machine-200x178.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/svm-support-vector-machine-425x378.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/svm-support-vector-machine-550x489.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/svm-support-vector-machine-115x102.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/svm-support-vector-machine.png 625w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Support Vector Machine (SVM) is a machine learning technique that takes classified data and looks at the extremes. Next, it draws a decision boundary line based on the data called a <strong>&#8220;hyperplane&#8221;<\/strong>. And the data points that the &#8220;hyperplane&#8221; margin pushes up against are the <strong>&#8220;support vectors&#8221;<\/strong>. <\/p>\n\n\n\n<p>And \u201csupport vectors\u201d are what&#8217;s important because they are the data points that are closest to the opposing classes. Because these points are the only ones considered, <strong>all other training points can be ignored in the model<\/strong>. Essentially, you feed SVM training samples of trees and grass. Based on this training data, it builds the model generating a decision boundary of its own.   <\/p>\n\n\n\n<p>Now, the results of this <a href=\"https:\/\/gisgeography.com\/image-classification-techniques-remote-sensing\/\" target=\"_blank\" rel=\"noopener noreferrer\">supervised classification<\/a> aren&#8217;t perfect and algorithms still have a lot more learning to do. We still need to work on features like roads, wetlands, and buildings. As algorithms get more training data, they will eventually improve to classify anywhere.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">2. Image Segmentation and Clustering with K-means<\/h3>\n\n\n\n<p>By far, the K-means algorithm is one of the most popular methods of clustering data. In K-means segmentation, it groups unlabeled data into the <strong>number of groups<\/strong> represented by the <strong>variable K<\/strong>.  <\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"235\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/obia-segmentation-clustering-ml-300x235.png\" alt=\"obia segmentation clustering ml\" class=\"wp-image-17593\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/obia-segmentation-clustering-ml-300x235.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/obia-segmentation-clustering-ml-50x39.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/obia-segmentation-clustering-ml-200x157.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/obia-segmentation-clustering-ml-425x333.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/obia-segmentation-clustering-ml-115x90.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/obia-segmentation-clustering-ml.png 486w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>This unsupervised learning approach iteratively assigns each data point into one of the K groupings based on the similarity of features. For example, similarity can be based on spectral characteristics and location.<\/p>\n\n\n\n<p>In <a href=\"http:\/\/gisgeography.com\/supervised-unsupervised-classification-arcgis\/\" target=\"_blank\" rel=\"noopener noreferrer\">unsupervised classification<\/a>, the k-means algorithm first segments the image for further analysis.  Next, each cluster is assigned a land cover class.<\/p>\n\n\n\n<p>However, GIS can use clustering in other unique ways. For example, data points could represent crime and you may want to cluster <strong>hot and low spots<\/strong> of crime.  Alternatively, you may want to segment based on socioeconomic, health, or environmental (like pollution) characteristics.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">3. Prediction Using Empirical Bayesian Kriging (EBK)<\/h3>\n\n\n\n<p>As you may know, <a href=\"https:\/\/gisgeography.com\/kriging-interpolation-prediction\/\">kriging interpolation<\/a> predicts unknown values based on spatial pattern. It estimates weights based on the variogram.  The quality of the estimated surface is reflected in the quality of the weights.  More specifically, you want weights that give an unbiased prediction and the smallest variance.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"216\" src=\"http:\/\/gisgeography.com\/wp-content\/uploads\/2017\/01\/kriging-results-300x216.png\" alt=\"kriging results\" class=\"wp-image-13749\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/01\/kriging-results-300x216.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/01\/kriging-results-50x36.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/01\/kriging-results-200x144.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/01\/kriging-results-425x305.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/01\/kriging-results-550x395.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/01\/kriging-results-115x83.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/01\/kriging-results-216x155.png 216w, https:\/\/gisgeography.com\/wp-content\/uploads\/2017\/01\/kriging-results.png 586w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Unlike kriging which fits one whole model for an entire data set, EBK kriging simulates at least one hundred <strong>local models by sub-setting the whole data set<\/strong>.  Because the model can morph itself locally to fit each individual semi-variogram using the kriging methodology, it overcomes the challenge of stationarity.<\/p>\n\n\n\n<p><a href=\"http:\/\/www.esri.com\/news\/arcuser\/1012\/empirical-byesian-kriging.html\" target=\"_blank\" rel=\"noopener noreferrer\">Empirical Bayesian Kriging (EBK)<\/a> predicts over and over again using a variety of simulations up to a hundred times.  Each semi-variogram varies from the other.  In the end, it mixes all of the semi-variograms for a final surface. You can\u2019t customize as you can with traditional kriging. <\/p>\n\n\n\n<p>Finally, it outputs what it thinks is the best solution.  Like a Monte Carlo analysis, it runs it repeatedly for you in the background. If it&#8217;s a random process, you let the random process run out over a thousand times. You see the trends in the resulting data and use that to justify your selection. This is why <strong>EBK almost always predicts better than straight kriging<\/strong>.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">The Process of Deep Learning and Training for Big Data<\/h3>\n\n\n\n<p>Whether you&#8217;re in GIS or another field, machine learning is all the buzz these days.  It\u2019s about distilling big data sets.  Because if you can let the computer detect the features, it will show you things you have never noticed. <\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright size-medium-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"425\" height=\"296\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/neural-network-machine-learning-2-425x296.png\" alt=\"neural network machine learning\" class=\"wp-image-17595\" style=\"width:319px;height:222px\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/neural-network-machine-learning-2-425x296.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/neural-network-machine-learning-2-300x209.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/neural-network-machine-learning-2-50x35.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/neural-network-machine-learning-2-200x139.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/neural-network-machine-learning-2-550x383.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/neural-network-machine-learning-2-115x80.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2018\/01\/neural-network-machine-learning-2.png 594w\" sizes=\"auto, (max-width: 425px) 100vw, 425px\" \/><\/figure>\n<\/div>\n\n\n<p>Because there&#8217;s too much data, you can uncover inherent patterns from it. The result is a trained neural network with just a set of weighted values.   <\/p>\n\n\n\n<p>When you train big data, this is when you&#8217;re going to need all the firepower you can get. But once you have the model trained, it\u2019s just a model with a set of weights in a file&#8230;  And this is why machine learning is a form of artificial intelligence &#8211; because you can train your data and then apply it to something entirely new and predict what it is.<\/p>\n\n\n\n<p>Overall, GIS uses machine learning for prediction, classification, and clustering.  AI and ML are still growing fields with a lot of frameworks still being developed daily.<\/p>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>GIS is applying machine learning into classification, prediction and segmentation. AI automatically learns on its own through training and labeling.<\/p>\n","protected":false},"author":2,"featured_media":17589,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"default","_kad_post_title":"default","_kad_post_layout":"default","_kad_post_sidebar_id":"","_kad_post_content_style":"default","_kad_post_vertical_padding":"default","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[90],"tags":[523],"class_list":["post-17580","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-gis-analysis","tag-data-science"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The Rise of Machine Learning and AI in GIS - GIS Geography<\/title>\n<meta name=\"description\" content=\"GIS is applying machine learning into classification, prediction and segmentation. 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