You’ve likely encountered the term “machine learning” (ML), but understanding its application within the realm of Geographic Information Systems (GIS) might seem a bit elusive. To put it simply, machine learning is the art of discerning meaningful patterns within noisy data – uncovering insights you might not have imagined existed. In essence, it’s software that creates software.
Instead of relying on pre-established functions, ML learns from repeated exposure to observed conditions, gradually building a model to apply in novel situations.
For instance, Google employs Bayesian classification to sift through spam emails, while Facebook utilizes it for facial recognition, automatically identifying faces in images. But how does ML fit into the world of GIS? Let’s delve into this inquiry today. And if you’re genuinely keen on delving into AI, we’ve got a selection of machine learning certification courses to kickstart your journey.
The Varied Landscape of Machine Learning (ML)
Machine learning can be broadly categorized into two realms: supervised and unsupervised learning, both of which find diverse applications within GIS. But what sets them apart?
Supervised Learning: This method involves fitting data to a predictive function. For instance, if you plot millions of sample points on a graph, you can approximate a function by drawing a line through them.
Unsupervised Learning: In contrast, unsupervised learning identifies patterns within unlabeled data. Imagine feeding millions of images into a training algorithm. After countless mathematical operations, it can analyze new pictures and group them into clusters.
Crucially, machine learning is about effectively solving problems. It autonomously learns and improves based on experience.
Lately, GIS has embraced artificial intelligence in areas like classification, prediction, and segmentation, with TensorFlow and PyTorch being two of the leading frameworks.
1. Image Classification Using Support Vector Machines (SVM)
When gazing at a satellite image, distinguishing between trees and grass or roads and buildings isn’t always straightforward – imagine the challenge for a computer.
Support Vector Machine (SVM) is a machine learning technique that takes classified data and identifies the extremes. It then establishes a decision boundary called a “hyperplane” based on this data. The data points that the “hyperplane” closely abuts are the “support vectors.”
These “support vectors” are pivotal because they represent the data points closest to opposing classes, rendering other training points irrelevant. Essentially, SVM is trained on samples of trees and grass, using this data to construct its own decision boundary.
While supervised classification with SVM isn’t flawless, algorithms continue to improve as they encounter more training data. Eventually, they’ll become adept at classifying various features, including roads, wetlands, and buildings.
2. Image Segmentation and Clustering with K-means
The K-means algorithm stands out as one of the most favored techniques for clustering data. In K-means segmentation, it groups unlabeled data into K groups, each represented by a variable.
This unsupervised learning approach iteratively assigns data points to one of the K groups based on feature similarity, which might include spectral characteristics and location.
In unsupervised classification, the K-means algorithm first segments the image for further analysis, with each cluster assigned a land cover class. Yet, GIS can harness clustering in diverse ways, such as grouping crime data to identify hotspots or segmenting based on socioeconomic, health, or environmental factors like pollution.
3. Prediction via Empirical Bayesian Kriging (EBK)
Kriging interpolation predicts unknown values based on spatial patterns, relying on weights determined by the variogram. The quality of the estimated surface hinges on these weights, aiming for unbiased predictions with minimal variance.
Unlike traditional kriging, which fits a single model to an entire dataset, EBK kriging employs multiple local models created by subsetting the dataset. Each semi-variogram varies, enabling it to adapt to individual spatial patterns, overcoming the challenge of stationarity.
Empirical Bayesian Kriging predicts repeatedly through numerous simulations, each time using a different semi-variogram. It amalgamates these semi-variograms to produce a final surface. Customization options are limited compared to traditional kriging, but EBK typically outperforms it due to its iterative nature.
In summary, it provides the best solution by running a Monte Carlo analysis numerous times, revealing trends in the resulting data, aiding in decision-making. This is why EBK often yields more accurate predictions than conventional kriging.
The Journey of Deep Learning and Big Data Training
Whether you’re immersed in GIS or another domain, machine learning is currently a hot topic. It excels at distilling insights from vast datasets. When you enable computers to detect hidden features, they unveil patterns you may never have noticed.
However, training with big data demands substantial computational power. But once you’ve trained your model, you possess a valuable asset: a model with a set of weighted values, ready to be applied to entirely new scenarios, predicting outcomes.
In essence, GIS employs machine learning for prediction, classification, and clustering. The fields of AI and ML are continuously evolving, with new frameworks emerging regularly, offering exciting prospects for the future.
