MAPPING AND ANALYZING URBAN GROWTH IN THE ABU DHABI METROPOLITAN USING GEOSPATIAL TECHNOLOGIES INTEGRATED WITH MACHINE LEARNING - Master thesis - 2025

Aug 5, 2026

Urbanization increases faster than population growth, intensifying environmental and social pressures while affecting regional climate. Rapid land-use and land-cover change drives these impacts, and high-resolution satellite imagery combined with geospatial tools enables accurate, efficient mapping over large areas. This thesis analyzes urban LULC change in the Abu Dhabi metropolitan area across 1990–2022 using geospatial technologies integrated with machine learning. Support Vector Machine models the change using Landsat imagery (1990, 2000, 2014, 2022), with Post-Classification Comparison for consecutive and full-period mapping. Results show high SVM accuracy and sustained urbanization, supporting planning and policy decisions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages75

Reading Time3 h 9 min

AccessFree to download

Abstract

Research problem and motivation

Data and methods (Landsat, SVM, PCC)

Study area and time span

Expected outcomes and key findings

United Arab Emirates University Scholarworks@UAEU | Theses | Electronic Theses and Dissertations | | --- | --- | 4-2024 MAPPING AND ANALYZING URBAN GROWTH IN THE ABU DHABI METROPOLITAN USING GEOSPATIAL TECHNOLOGIES INTEGRATED WITH MACHINE LEARNING Hamsa Mohamed Yusuf Follow this and additional works at: [https://scholarworks.uaeu.ac.ae/all_theses](https://scholarworks.uaeu.ac.ae/all_theses) Part of the Urban Studies Commons MASTER THESIS NO. 2025: 50 College of Humanities and Social Sciences Department of Geography and Urban Sustainability MAPPING AND ANALYZING URBAN GROWTH IN THE ABU DHABI METROPOLITAN USING GEOSPATIAL TECHNOLOGIES INTEGRATED WITH MACHINE LEARNING Hamsa Mohamed Yusuf United Arab Emirates University College of Humanities and Social Sciences Department of Geography and Urban Sustainability MAPPING AND ANALYZING URBAN GROWTH IN THE ABU DHABI METROPOLITAN USING GEOSPATIAL TECHNOLOGIES INTEGRATED WITH MACHINE LEARNING Hamsa Mohamed Yusuf This thesis is submitted in partial fulfilment of the requirements for the degree of Master of Science in Remote Sensing and Geographic Information Systems April 2025 United Arab Emirates University Master Thesis 2025: 50 Cover: Urban land change of Abu Dhabi for the years 1990/2000/2014/2022 generated using ArcGIS. U represents urban land and N represents non-urban land (Photo: By Hamsa Mohamed Yusuf) © 2025 Hamsa Mohamed Yusuf, AlAin, UAE All Rights Reserved Print: University Print Service, UAEU 2025 Declaration of Original Work I, Hamsa Mohamed Yusuf, the undersigned, a graduate student at the United Arab Emirates University (UAEU), and the author of this thesis entitled “Mapping and Analyzing Urban Growth in the Abu Dhabi Metropolitan Using Geospatial Technologies Integrated with Machine Learning”, hereby, solemnly declare that this is the original research work done by me under the supervision of Dr. Nazmi Saleous, in the College of Humanities and Social Sciences at UAEU. This work has not previously formed the basis for the award of any academic degree, diploma or a similar title at this or any other university. Any materials borrowed from other sources (whether published or unpublished) and relied upon or included in my thesis have been properly cited and acknowledged in accordance with appropriate academic conventions. I further declare that there is no potential conflict of interest with respect to the research, data collection, authorship, presentation and/or publication of this thesis. Student’s Signature: Date: May 6, 2025 Approval of the Master Thesis This Master Thesis is approved by the following Examining Committee Members: 1) Advisor (Committee Chair): Dr. Nazmi Saleous Title: Associate Professor Department of Geography and Urban Sustainability College of Humanities and Social Sciences Signature Date 29/4/2025 2) Member: Dr. Khalid Hussein Title: Associate Professor Department of Geography and Urban Sustainability College of Humanities and Social Sciences Signature Date 29/4/2025 3) Member (External Examiner): Dr. Abdelgadir Abuelgasim Title: Associate Professor Department of Defense and Security Institution: Rabdan Academy, UAE Signature Date 29/4/2025 This Master Thesis is accepted by: Dean of the College of Humanities and Social Sciences: Professor Mohamed Bin Huwaidin Signature Date 18/6/2025 Dean of the College of Graduate Studies: Professor Ali Al-Marzouqi Signature Date 19/06/2025 Abstract Urbanization is happening at a rate twice the increase in population on a worldwide scale. This has great environmental and social impacts, along with large impacts on regional climate and one of the reasons for this is the ever-continuous change of land use and land cover. The availability of high-resolution satellite imagery in addition to the advancements in geospatial technology allow mapping of LULC changes to be done accurately, efficiently and cove

FAQs

What problem does the thesis address?

The thesis addresses how rapid urbanization and continuous land-use/land-cover change can be mapped and modeled accurately over time.

Which machine learning method is used to model urban growth?

Support Vector Machine (SVM) is used to model urban LULCC in the Abu Dhabi metropolitan area.

What data and mapping strategy support the analysis?

Landsat imagery for 1990, 2000, 2014, and 2022 is analyzed, and Post-Classification Comparison (PCC) maps change between consecutive years and across the full study period.

Aug 5, 2026

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