Object-based random forest classification for informal settlements identification in the Middle East: Jeddah a case study. Issue 11 (2nd June 2020)

• Record Type: Journal Article
• Authors: Fallatah, Ahmad; Jones, Simon; Mitchell, David
• Is Part Of: International journal of remote sensing. Volume 41:Issue 11(2020)
• Page Start: 4421
• Page End: 4445
• Publication Date: 2020-06-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2020.1718237
• Languages: English
• ISSNs: 0143-1161

Abstract

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.