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Open AccessArticle

Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia

1
Faculty of Geo-Information Science & Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
2
Ministry of Public Works and Housing of Indonesia, Jalan Pattimura No.20, Jakarta Selatan 12110, DKI Jakarta, Indonesia
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1522; https://doi.org/10.3390/rs10101522
Received: 31 July 2018 / Revised: 5 September 2018 / Accepted: 19 September 2018 / Published: 22 September 2018
(This article belongs to the Special Issue Machine Learning Applications in Earth Science Big Data Analysis)
The survey-based slum mapping (SBSM) program conducted by the Indonesian government to reach the national target of “cities without slums” by 2019 shows mapping inconsistencies due to several reasons, e.g., the dependency on the surveyor’s experiences and the complexity of the slum indicators set. By relying on such inconsistent maps, it will be difficult to monitor the national slum upgrading program’s progress. Remote sensing imagery combined with machine learning algorithms could support the reduction of these inconsistencies. This study evaluates the performance of two machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF), for slum mapping in support of the slum mapping campaign in Bandung, Indonesia. Recognizing the complexity in differentiating slum and formal areas in Indonesia, the study used a combination of spectral, contextual, and morphological features. In addition, sequential feature selection (SFS) combined with the Hilbert–Schmidt independence criterion (HSIC) was used to select significant features for classifying slums. Overall, the highest accuracy (88.5%) was achieved by the SVM with SFS using contextual, morphological, and spectral features, which is higher than the estimated accuracy of the SBSM. To evaluate the potential of machine learning-based slum mapping (MLBSM) in support of slum upgrading programs, interviews were conducted with several local and national stakeholders. Results show that local acceptance for a remote sensing-based slum mapping approach varies among stakeholder groups. Therefore, a locally adapted framework is required to combine ground surveys with robust and consistent machine learning methods, for being able to deal with big data, and to allow the rapid extraction of consistent information on the dynamics of slums at a large scale. View Full-Text
Keywords: machine learning; slums; slum upgrading programs; Bandung; Indonesia machine learning; slums; slum upgrading programs; Bandung; Indonesia
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MDPI and ACS Style

Leonita, G.; Kuffer, M.; Sliuzas, R.; Persello, C. Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia. Remote Sens. 2018, 10, 1522.

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