Next Article in Journal
Sparse Unmixing of Hyperspectral Data with Noise Level Estimation
Next Article in Special Issue
A Combined Approach to Classifying Land Surface Cover of Urban Domestic Gardens Using Citizen Science Data and High Resolution Image Analysis
Previous Article in Journal
Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data
Previous Article in Special Issue
Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(11), 1164; https://doi.org/10.3390/rs9111164

Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia

Faculty of Geo-Information Science & Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 27 September 2017 / Revised: 7 November 2017 / Accepted: 10 November 2017 / Published: 13 November 2017
(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)
Full-Text   |   PDF [11579 KB, uploaded 14 November 2017]   |  

Abstract

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification. View Full-Text
Keywords: accuracy; uncertainties; object-based; slums; Jakarta accuracy; uncertainties; object-based; slums; Jakarta
Figures

Graphical abstract

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. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Pratomo, J.; Kuffer, M.; Martinez, J.; Kohli, D. Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia. Remote Sens. 2017, 9, 1164.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top