Object-based random forest classification for informal settlements identification in the Middle East: Jeddah a case study

Fallatah, Ahmad; Jones, Simon; Mitchell, David

doi:10.1080/01431161.2020.1718237

Cited by 24 publications

(33 citation statements)

References 73 publications

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“…This has resulted in leaving these settlements undocumented and overlooked on official maps (Kamalipour, 2020). Many studies have suggested the application of VHR to detect and map informal settlements (Fallatah et al, 2020;Persello & Stein, 2017). However, VHR is, as stated earlier, very expensive and might not be accessible to all.…”

Section: Discussionmentioning

confidence: 99%

“…In several studies OBC has been used to detect informal settlements through HR/VHR classification based on defining a group of image-based proxies to differentiate land-use classes (Fallatah et al, 2020;Hernandez, Ruiz, & Shi, 2018;Hofmann et al, 2008;Ranguelova et al, 2019). On the other hand, PBC, that effectively classifies MR, has rarely been applied to detect informal settlements.…”

Section: Satellite Image Classification For Informal Settlement Detectionmentioning

confidence: 99%

“…In recent decades, the use of satellite imagery has been increasingly investigated to estimate urban population and density (Wang, Fan, & Wang, 2020;Lu, Jungho, Quackenbush, & Halligan, 2010;Hillson et al, 2019;Ogrosky, 1975;Dittakan, Coenen, Christley, & Wardeh, 2013), mapping land cover and land-use in urban areas (Carleer & Wolff, 2006;Gamba et al, 2007;Movia, Beinat, & Crosilla, 2016;Novack et al, 2003;Ouyang et al, 2011;Voorde, Van, William, & Canters, 2007), and also for tracking changes in the urban landscape (Volpi, Tuia, Bovolo, Kanevski, & Bruzzone, 2013;Zhang, Peng, & Huang, 2018;Lv, Liu, & Benediktsson, 2020). Remote sensing has also been used to detect informal settlements in urban areas, which is the first step to upgrade the most vulnerable communities (Fallatah, Jones, & Mitchell, 2020;Weir, McQuillan, & Francis, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

2021

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The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs. Very High-Resolution satellite images (VHR), have been extensively used for this purpose. However, as a cost-prohibitive data source, VHR might not be available to all, particularly nations that are home to many informal settlements. This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements. Here, in a case study of Jakarta, Indonesia, Medium Resolution satellite imagery (MR) derived from Landsat 8 (2020) was classified to detect these settlements. The classification was done using Random Forest (RF) classifier through two complementary approaches to develop the training set. In the first approach, available survey data sets (Jakarta's informal settlements map for 2015) and visual interpretation using High-Resolution Google Map imagery have been used to build the training set. Throughout the second round of classification, OpenStreetMap (OSM) layers were used as the complementary approach for training. Results from the validation test for the second round revealed better accuracy and precision in classification. The proposed method provides an opportunity to use open data for informal settlements detection, when: 1) more expensive high resolution data sources are not accessible; 2) the area of interest is not larger than a city; and 3) the physical characteristics of the settlements differ significantly from their surrounding formal area. The method presents the application of globally accessible data to help the achievement of resilience and SDGs in informal settlements.

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Section: Discussionmentioning

confidence: 99%

Section: Satellite Image Classification For Informal Settlement Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

2021

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“…While the body of EO literature about deprived area mapping is rapidly increasing (e.g., [50,51,[53][54][55][56][57][58][59][60][61][62][63][64][65]), several challenges in this area still exist. For example, most studies are not addressing global information needs (e.g., producing data in support of SDG 11 [66]), with most deprived area mapping approaches mainly focusing on small areas below the city scale and for very specific sites.…”

Section: Introductionmentioning

confidence: 99%

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

et al. 2020

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Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.

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“…It's main purpose is build a proper classification model to analyze the effective information hidden in the students' achievements. As for now, there are many classification methods, such as moment invariants [1], random forest [2,3], support vector machine [4], principal component analysis [5], Markov random fields [6], particle swarm optimization [7], discrete wavelet transform [8], et al Additionally, in [9], a novel framework of complex network classifier is proposed to tackle the problem of network classification, which shows that the proposed method performs well on large-scale networks. In [10], it uses the domain-adversarial learning method to classify the low-resource text, and it can obtain good results.…”

Section: Introductionmentioning

confidence: 99%

Education quality detection method based on the probabilistic neural network algorithm

Wu¹,

Jiang²,

Wang³

et al. 2020

Diagnostyka

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Object-based random forest classification for informal settlements identification in the Middle East: Jeddah a case study

Cited by 24 publications

References 73 publications

Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

Education quality detection method based on the probabilistic neural network algorithm

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