A Spatiotemporal Ontology of Informal Settlements using a combination of OBIA-RF with Worldview-3 and Landsat data
An understanding of the spatial distribution of informal settlements within a city is important for urban management decision-making and service infrastructure provision and provides useful information for planners and policymakers and has a role in minimising future urban environmental issues. The...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
IEEE
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/95821 |
| _version_ | 1848766049801994240 |
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| author | Alrasheedi, Khlood Ghalibr Dewan, Ashraf El-Mowafy, Ahmed |
| author_facet | Alrasheedi, Khlood Ghalibr Dewan, Ashraf El-Mowafy, Ahmed |
| author_sort | Alrasheedi, Khlood Ghalibr |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | An understanding of the spatial distribution of informal settlements within a city is important for urban management decision-making and service infrastructure provision and provides useful information for planners and policymakers and has a role in minimising future urban environmental issues. The objective of this work is to evaluate the performance of an ontology of informal settlements mapping for Riyadh city. Satellite data include a combination of medium-resolution Landsat thematic mapper (TM), enhanced thematic mapper plus (ETM+) and operational land imager (OLI) and VHR Worldview-3, imagery. Object-based image analysis (OBIA) technique was employed to identify thirty useful indicators at defined object, settlement, environment, and temporal levels. Time series analysis (TSA) was undertaken, and a multi-dimensional model was developed to define the trend of changes through 30 years. The classification process incorporated OBIA, random forest (RF) and Landtrendr techniques. The classification output included delineation of formal and informal settlement boundaries and road networks, as well as vegetated and vacant areas. The final object-based random forest (OBIA-RF) and TSA classification demonstrated an overall accuracy of 89% with the corresponding kappa value of 87%. The OBIA-RF classification developed without TSA techniques returned an overall accuracy of 87% and kappa value of 84%. The study indicated that using OBIA and RF methods, in combination with Landtrendr, can be a useful tool for planners and decision-makers to identify changes in the land cover of informal settlements within Riyadh city and beyond. |
| first_indexed | 2025-11-14T11:44:58Z |
| format | Journal Article |
| id | curtin-20.500.11937-95821 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:44:58Z |
| publishDate | 2024 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-958212024-10-15T04:24:55Z A Spatiotemporal Ontology of Informal Settlements using a combination of OBIA-RF with Worldview-3 and Landsat data Alrasheedi, Khlood Ghalibr Dewan, Ashraf El-Mowafy, Ahmed An understanding of the spatial distribution of informal settlements within a city is important for urban management decision-making and service infrastructure provision and provides useful information for planners and policymakers and has a role in minimising future urban environmental issues. The objective of this work is to evaluate the performance of an ontology of informal settlements mapping for Riyadh city. Satellite data include a combination of medium-resolution Landsat thematic mapper (TM), enhanced thematic mapper plus (ETM+) and operational land imager (OLI) and VHR Worldview-3, imagery. Object-based image analysis (OBIA) technique was employed to identify thirty useful indicators at defined object, settlement, environment, and temporal levels. Time series analysis (TSA) was undertaken, and a multi-dimensional model was developed to define the trend of changes through 30 years. The classification process incorporated OBIA, random forest (RF) and Landtrendr techniques. The classification output included delineation of formal and informal settlement boundaries and road networks, as well as vegetated and vacant areas. The final object-based random forest (OBIA-RF) and TSA classification demonstrated an overall accuracy of 89% with the corresponding kappa value of 87%. The OBIA-RF classification developed without TSA techniques returned an overall accuracy of 87% and kappa value of 84%. The study indicated that using OBIA and RF methods, in combination with Landtrendr, can be a useful tool for planners and decision-makers to identify changes in the land cover of informal settlements within Riyadh city and beyond. 2024 Journal Article http://hdl.handle.net/20.500.11937/95821 10.1109/JSTARS.2024.3450844 http://creativecommons.org/licenses/by-nc-nd/4.0/ IEEE fulltext |
| spellingShingle | Alrasheedi, Khlood Ghalibr Dewan, Ashraf El-Mowafy, Ahmed A Spatiotemporal Ontology of Informal Settlements using a combination of OBIA-RF with Worldview-3 and Landsat data |
| title | A Spatiotemporal Ontology of Informal Settlements
using a combination of OBIA-RF with Worldview-3
and Landsat data |
| title_full | A Spatiotemporal Ontology of Informal Settlements
using a combination of OBIA-RF with Worldview-3
and Landsat data |
| title_fullStr | A Spatiotemporal Ontology of Informal Settlements
using a combination of OBIA-RF with Worldview-3
and Landsat data |
| title_full_unstemmed | A Spatiotemporal Ontology of Informal Settlements
using a combination of OBIA-RF with Worldview-3
and Landsat data |
| title_short | A Spatiotemporal Ontology of Informal Settlements
using a combination of OBIA-RF with Worldview-3
and Landsat data |
| title_sort | spatiotemporal ontology of informal settlements
using a combination of obia-rf with worldview-3
and landsat data |
| url | http://hdl.handle.net/20.500.11937/95821 |