Ontology of Informal Settlements in Riyadh, Saudi Arabia with Geospatial Intelligence
This study aims to integrate local knowledge, remote sensing data, and machine learning to investigate and develop an informal settlements ontology for use within the Arabian Peninsula region. Information used included very high to medium resolution satellite images, field surveys, expert opinion re...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/96025 |
| _version_ | 1848766076028977152 |
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| author | Alrasheedi, Khlood Ghalib |
| author_facet | Alrasheedi, Khlood Ghalib |
| author_sort | Alrasheedi, Khlood Ghalib |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This study aims to integrate local knowledge, remote sensing data, and machine learning to investigate and develop an informal settlements ontology for use within the Arabian Peninsula region. Information used included very high to medium resolution satellite images, field surveys, expert opinion regarding local conditions, and a wide range of geographic data. Object-based image analysis, machine learning methods, expert knowledge, and various geographic datasets were employed to identify the distribution of informal settlements over time and space. |
| first_indexed | 2025-11-14T11:45:23Z |
| format | Thesis |
| id | curtin-20.500.11937-96025 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:45:23Z |
| publishDate | 2024 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-960252024-10-08T01:52:44Z Ontology of Informal Settlements in Riyadh, Saudi Arabia with Geospatial Intelligence Alrasheedi, Khlood Ghalib This study aims to integrate local knowledge, remote sensing data, and machine learning to investigate and develop an informal settlements ontology for use within the Arabian Peninsula region. Information used included very high to medium resolution satellite images, field surveys, expert opinion regarding local conditions, and a wide range of geographic data. Object-based image analysis, machine learning methods, expert knowledge, and various geographic datasets were employed to identify the distribution of informal settlements over time and space. 2024 Thesis http://hdl.handle.net/20.500.11937/96025 Curtin University restricted |
| spellingShingle | Alrasheedi, Khlood Ghalib Ontology of Informal Settlements in Riyadh, Saudi Arabia with Geospatial Intelligence |
| title | Ontology of Informal Settlements in Riyadh, Saudi Arabia with
Geospatial Intelligence |
| title_full | Ontology of Informal Settlements in Riyadh, Saudi Arabia with
Geospatial Intelligence |
| title_fullStr | Ontology of Informal Settlements in Riyadh, Saudi Arabia with
Geospatial Intelligence |
| title_full_unstemmed | Ontology of Informal Settlements in Riyadh, Saudi Arabia with
Geospatial Intelligence |
| title_short | Ontology of Informal Settlements in Riyadh, Saudi Arabia with
Geospatial Intelligence |
| title_sort | ontology of informal settlements in riyadh, saudi arabia with
geospatial intelligence |
| url | http://hdl.handle.net/20.500.11937/96025 |