In this module, you will explore how spatial patterns differ from random distributions and why that difference matters in real-world analysis. Using air-quality sensor data as a motivating example, you will examine how Global Moran’s I quantifies spatial autocorrelation in polygon data and helps analysts identify clustering patterns that might otherwise go unnoticed.

In this module, you will examine how spatial analysts estimate values between discrete measurement locations. Using air-quality sensor data as a motivating example, you will be introduced to Inverse Distance Weighting (IDW) interpolation and learn how distance-based assumptions are used to generate continuous surfaces from point observations. You will explore how parameter choices influence interpolation results and learn how to interpret estimated surfaces responsibly in real-world spatial analysis contexts.

In this module, you will step back from computation to interpretation, focusing on semivariograms as diagnostic tools for spatial structure. By learning how to read range, sill, and nugget, you will gain intuition about spatial dependence, knowledge that informs both analysis choices and communication with non-technical audiences.

Learners understand what LiDAR point clouds represent and can confidently load and explore them in a 3D environment.

Learners understand why DEMs are derived products and can create one correctly from ground-class LiDAR points.

Learners evaluate whether a DEM is fit for purpose by comparing it against known reference elevations.

You will explore why raw imagery alone is insufficient for supervised classification and how engineered features improve model performance. The lesson focuses on practical extraction of spectral bands and texture metrics used in land-cover analysis.

You will apply engineered features to train a Random Forest classifier. Emphasis is placed on intuition: how trees vote, how parameters affect performance, and how to avoid beginner mistakes.

You will evaluate whether the model meets job requirements by interpreting confusion matrices and accuracy metrics. The lesson emphasizes decision-making, not just calculation.

In this module, you will apply transfer learning techniques to fine-tune a pre-trained convolutional neural network (CNN) for land cover classification using satellite imagery. The module focuses on adapting existing vision models to geospatial data under real-world constraints such as limited labeled samples, class imbalance, and spatial generalization challenges.

In this module, learners design and apply data augmentation pipelines to improve the generalization of convolutional neural networks trained on satellite imagery. The module focuses on selecting realistic augmentations that preserve spatial meaning while addressing limited and imbalanced land-cover data.

In this module, learners use Grad-CAM visualizations to interpret convolutional neural network predictions for satellite imagery. The module emphasizes understanding model attention, identifying failure modes, and communicating model behavior clearly to technical and non-technical stakeholders.

In this project, you will build a geospatial machine learning workflow to classify land cover using imagery, LiDAR-derived elevation data, and labeled samples. You will engineer features, train a model, validate the results, and generate a classified land cover output. You will also summarize model performance and create an interpretation output to explain how the model behaves. This project requires learners to demonstrate spatial analysis, 3D data use, machine learning implementation, validation, interpretation, and stakeholder communication in one authentic workflow.