Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India

Ranguelova, Elena; Weel, Berend; Roy, Deblina; Kuffer, Monika; Pfeffer, Karin; Lees, Michael

doi:10.1080/22797254.2018.1535838

Cited by 27 publications

(11 citation statements)

References 40 publications

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“…This allows the extraction of densely built-up areas with low GLCM variance values, as they have a high probability to be slums. WorldView-2 scenes (PAN: 0.5 m and MS: 2 m) from 2010 were used for covering a purposefully selected part of the city which has a good mix of slum and non-slum neighbourhoods (Ranguelova et al, 2018). In order to validate the SSI, we calculated the GLCM variance from VHR imageries for each block using an x and y shift value of (1, 1) and a window size of 21 pixels × 21 pixels, and the Pearson correlation was calculated between the SSI and the GLCM variance.…”

Section: Resultsmentioning

confidence: 99%

An exploratory factor analysis model for slum severity index in Mexico City

2019

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Today over half of the world population live in urban areas and it is projected that by 2050, two out of three people will live in a city. This increased rural-urban migration coupled with housing poverty has led to the growth and formation of informal settlements commonly known as slums. In Mexico, 25 percent of urban population now live in informal settlements with varying degree of depravity. Although some informal neighbourhoods have contributed to the upward mobility of the inhabitants, but the majority still lack basic services. Mexico City and the conurbation around it, forms a mega city of 21 million people that has been growing in a manner qualified as "highly unproductive, (that) deepens inequality, raises pollution levels" and contains the largest slum in the world, Neza-Chalco-Izta. Urban reforms are now aiming to better the conditions in these slums and therefore it is very important to have reliable measurement tools to assess the changes that are undergoing. In this paper, we use exploratory factor analysis to define an index of depravity in Mexico City, namely the Slum Severity Index (SSI), based on the UN-HABITATs definition of slum. We apply this novel approach to the Census survey of Mexico and measure the housing deprivation levels types from 1990 -2010. The analysis highlights high variability in housing conditions within Mexico City. We find that the SSI decreased significantly between 1990 -2000 due to several policy reforms, but increased between 2000 -2010. We also show correlations of the SSI with other social factors such as education, health and migration. We present a validation of the SSI using Grey Level co-occurrence Matrix (GLCM) features extracted arXiv:1807.06994v1 [stat.AP] 18 Jul 2018 from Very-High Resolution (VHR) remote-sensed satellite images. Finally, we show that the SSI can present a cardinally meaningful assessment of the extent of the difference in depravity as compared to a similar index defined by CONEVAL, a government institution that studies poverty in Mexico.

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Section: Resultsmentioning

confidence: 99%

An exploratory factor analysis model for slum severity index in Mexico City

2019

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“…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%

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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“…Slum detection and extraction from remote sensing images was also the focus of several studies [40]. While early studies in this topic used traditional image processing methods [32,[41][42][43], recent ones used more advanced machine learning methods [44] such as SVM [45,46], Random Forest [47], and CNN-based approaches [48,49]. For example, Kuffer et al [50] used gray-level co-occurrence matrix (GLCM) features to extract slum areas from very high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery

Ghaffarian

Emtehani

2021

Climate

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Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support decision-makers and urban planners, especially in case of disaster recovery. Here, we developed an approach to monitor the urban deprived areas over a four-year period after super Typhoon Haiyan, which struck Tacloban city, in the Philippines, in 2013, using high-resolution satellite images and machine learning methods. A Support Vector Machine classification method supported by a local binary patterns feature extraction model was initially performed to detect slum areas in the pre-disaster, just after/event, and post-disaster images. Afterward, a dense conditional random fields model was employed to produce the final slum areas maps. The developed method detected slum areas with accuracies over 83%. We produced the damage and recovery maps based on change analysis over the detected slum areas. The results revealed that most of the slum areas were reconstructed 4 years after Typhoon Haiyan, and thus, the city returned to the pre-existing vulnerability level.

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Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India

Cited by 27 publications

References 40 publications

An exploratory factor analysis model for slum severity index in Mexico City

An exploratory factor analysis model for slum severity index in Mexico City

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

Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery

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