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Object-based random forest classification for informal settlements identification in the Middle East: Jeddah a case study.
- Source :
- International Journal of Remote Sensing; Jun2020, Vol. 41 Issue 11, p4421-4445, 25p, 1 Diagram, 5 Charts, 1 Graph, 6 Maps
- Publication Year :
- 2020
-
Abstract
- The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical in efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal settlement areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorized according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a processing chain approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels. [ABSTRACT FROM AUTHOR]
- Subjects :
- CITIES & towns
IMAGE analysis
URBAN policy
RANDOM forest algorithms
CASE studies
Subjects
Details
- Language :
- English
- ISSN :
- 01431161
- Volume :
- 41
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- International Journal of Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 141750809
- Full Text :
- https://doi.org/10.1080/01431161.2020.1718237