Show simple item record

dc.contributor.authorAlrasheedi, Khlood
dc.contributor.authorDewan, Ashraf
dc.contributor.authorEl-Mowafy, Ahmed
dc.identifier.citationDewan, A. and Alrasheedi, K. and El-Mowafy, A. 2023. Mapping Informal Settlements Using Machine Learning Techniques, Object-Based Image Analysis and local Knowledge. In: International Geoscience and Remote Sensing Symposium (IGARSS), 16-21 July 2023, Pasadena, California.

The existence of informal settlements in Riyadh City, the Kingdom of Saudi Arabia (KSA), has given rise to some urban planning issues. To provide improvements to mapping and planning processes, the current study aims to evaluate and characterize informal settlements within the city using object-based machine learning (ML) techniques (specifically, Random Forest (RF) and Support Vector Machine (SVM)), expert knowledge (EK) and satellite data. An examination of four defined locales has produced a comprehensive, local, informal settlement ontology. Four main categories (shape, geometry, texture, and pattern) were used to build the ontological framework. Expert local knowledge was employed to produce a ruleset to accurately identify and map these areas. Specific indicators identified by the specialists were used in a combined object-based ML and image analysis (OBIA) approach, with high-resolution worldview-3 imagery used as input data. Results demonstrated that combining EK and ML with remotely sensed data can efficiently, effectively and accurately distinguish informal settlement areas. This work has shown that an object-based ML technique (RF), in combination with EK about important local environment indicators, provides a useful method for mapping informal settlements.

dc.subjectMapping; GIS, Informal settlements, SA
dc.titleMapping Informal Settlements Using Machine Learning Techniques, Object-Based Image Analysis and local Knowledge
dc.typeConference Paper
dcterms.source.conferenceInternational Geoscience and Remote Sensing Symposium (IGARSS)
dcterms.source.conference-start-date16 Jul 2023
dcterms.source.conferencelocationPasadena, California.
curtin.departmentSchool of Earth and Planetary Sciences (EPS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidEl-Mowafy, Ahmed [0000-0001-7060-4123]
dcterms.source.conference-end-date21 Jul 2023
curtin.contributor.scopusauthoridEl-Mowafy, Ahmed [7004059531]

Files in this item


This item appears in the following Collection(s)

Show simple item record