Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Lane Department of Computer Science and Electrical Engineering

Committee Chair

Donald Adjeroh

Committee Member

Michael Strager

Committee Member

Gianfranco Doretto


In studying dynamic and complex processes which are influenced by a system of inter-connected driving variables, it is crucial to apply models that can learn the complexity of the interactions. Land transformation is one of such complex processes, prediction of which can help to mitigate severe climate situations and improve the resiliency of communities. In this study, a multi-spectral set of data cubes is used to capture various characteristics of a geographic region. Based on the data cube, a feature space is constructed using socio-economic attributes, terrain characteristics, and landscape traits of the study region. Two-dimensional and three-dimensional convolutional neural networks (CNNs) and ensemble methods are then applied on this feature space to investigate model performance and improve the robustness of the models. This research demonstrates an application of evidence fusion at feature, decision and score levels, and applies ensemble random forests for post processing. Performance is assessed using the Dice coefficient, Receiver Operating Characteristic (ROC) curves, data visualization, and running time. This study shows that the implementation of ensemble models improved the accuracy of the models in terms of model stability, precision and recall, and dice coefficient.