Studying the growth pattern of cities/urban areas has received considerable attention during the past few decades. The goal is to identify directions and locations of potential growth, assess infrastructure and public service requirements, and ensure the integration of the new developments with the existing city structure. This dissertation presents a novel model for urban growth prediction using a novel machine learning model. The model treats successive historical satellite images of the urban area under consideration as a video for which future frames are predicted. A time-dependent convolutional encoder-decoder architecture is adopted. The model considers as an input a satellite image for the base year and the prediction horizon. It constructs an image that predicts the growth of the urban area for any given target year within the specified horizon. A sensitivity analysis is performed to determine the best combination of parameters to obtain the highest prediction performance. As a case study, the model is used to predict the urban growth pattern for the Dallas-Fort Worth (DFW) area in Texas, with focus on two of its counties that observed significant growth over the past decade. In addition, the model is applied to predict the growth pattern of five cities in the Middle East and North Africa (MENA) region. These cities vary in terms of their size, population, historical heritage, level of control applied to their growth, geographical locations, complexity of their structure, and socio-economic characteristics. The model is shown to produce results that are consistent with other growth prediction studies conducted for these cities.

Degree Date

Summer 8-4-2020

Document Type


Degree Name



Civil and Environmental Engineering


Dr. Khaled Abdelghany

Second Advisor

Dr. Barbara Minsker

Third Advisor

Dr. Brett Story

Fourth Advisor

Dr. Janille Smith-Colin

Fifth Advisor

Dr. Mohammad Khodayar

Subject Area

Computer Science, Geography, Urban Planning

Number of Pages




Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License