Machine Learning in GIS and Remote Sensing: 5 Courses in 1

Learn how to install and manage QGIS plugins, including version considerations, and explore examples like the Sentinel plugin and the semi-automatic classification plugin for remote sensing analysis.

Explores how artificial intelligence, machine learning, and deep learning apply to GIS and remote sensing, including classification, clustering, prediction, and computer vision with satellite and drone imagery.

Explore supervised and unsupervised image classification algorithms, including Kamins clustering, minimum distance to mean, maximum likelihood, Bayesian, decision trees, random forests, and support vector machines, with examples and accuracy considerations.

Explore regression analysis to estimate relationships between dependent and independent variables, using linear and multivariate models for prediction and causal inference, with examples like crime hotspots and disease prevalence.

Explore image classification in ArcGIS, covering unsupervised and supervised pixel-based methods, as well as object-based approaches, with hands-on steps for training samples, signature files, and post-classification cleanup.

Explore ArcGIS Pro desktop in this course, with emphasis on free trial access, version 10.6 or higher compatibility, and tips to locate functions via search plus available help resources.

Perform unsupervised image classification in ArcGIS using a multi-band Sentinel composite. Set class count and sampling to reveal agricultural fields, harvested fields, and built-up areas for subsequent supervised classification.

Follow a step-by-step guide to install the orfeo toolbox plug-in for qgis, activate the toolbox panel, and access machine learning algorithms for image classification.

Apply unsupervised classification in QGIS using k-means clustering to segment a city image into three classes—water, vegetation, and built-up—adjusting input, output, and rendering results.

Select training data in ArcMap 10.6 by outlining cropland, water, trees, and settlements with polygons, label each area, and save the training samples to build a supervised classification model.

Apply supervised image classification for land use land cover with a support vector machine in ArcGIS, using training data and local processing; compare SVM with random forest results.

Perform land use and land cover classification on a Landsat 5 image using the maximum likelihood algorithm, with training data and polygons, and assess results with color coded classes.

Run a land use land cover classification with the minimum distance algorithm on forests, soil, and water, compare with maximum likelihood, and note that visual assessment favors maximum likelihood.

Explore accuracy assessment of image classification in GIS using confusion matrices and reference data. Learn visual and quantitative controls, and compute overall accuracy, user accuracy, and producer accuracy.

Create validation data and perform accuracy assessment for land use land cover maps from Landsat imagery, applying visual checks and numeric validation across vegetation, soil, and water classes.

Perform LULC accuracy assessment in QGIS with the semi-automatic classification plugin, using training and validation data to generate an accuracy report showing producer, user, and overall accuracies.

Learn how images are segmented for GIS and remote sensing, showing how adjacent pixels form segments via spectral details to improve classification.