Version 1
: Received: 12 September 2018 / Approved: 12 September 2018 / Online: 12 September 2018 (12:32:25 CEST)
How to cite:
Fallatah, A.; Jones, S.; Mitchell, D. Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach. Preprints2018, 2018090219. https://doi.org/10.20944/preprints201809.0219.v1
Fallatah, A.; Jones, S.; Mitchell, D. Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach. Preprints 2018, 2018090219. https://doi.org/10.20944/preprints201809.0219.v1
Fallatah, A.; Jones, S.; Mitchell, D. Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach. Preprints2018, 2018090219. https://doi.org/10.20944/preprints201809.0219.v1
APA Style
Fallatah, A., Jones, S., & Mitchell, D. (2018). Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach. Preprints. https://doi.org/10.20944/preprints201809.0219.v1
Chicago/Turabian Style
Fallatah, A., Simon Jones and David Mitchell. 2018 "Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach" Preprints. https://doi.org/10.20944/preprints201809.0219.v1
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 to efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal 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 categorised 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 hybrid approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.
Keywords
informal settlement indicators; very high resolution (VHR); urbanisation; sustainable development goals; object-based image analysis (OBIA); machine learning (ML); random forest (RF)
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
